Dan Keller 0:07

Welcome to this episode of Substantial Matters: Life and Science of Parkinson's. I'm your host, Dan Keller. At the Parkinson's Foundation, we want all people with Parkinson's and their families to get the care and support they need. Better care starts with better research and leads to better lives. In this podcast series, we highlight the fruits of that research—the treatments and techniques that can help you live a better life now, as well as research that can bring a better tomorrow.

Most people with Parkinson's disease take medications to alleviate symptoms, but along with every drug comes the possibility of adverse effects, also commonly called side effects. These unwanted side effects to medications can be minor or more troublesome, such as dyskinesias and hallucinations, so most therapies are a balance, minimizing the downsides of a drug to increase their tolerability, making people more likely to take them, and thus increasing their benefits. The good news is that side effects do not affect everyone taking a certain drug, and moreover, they can often be minimized or alleviated by working with your healthcare team.

I spoke with neurologist Dr. Irene Richard of the University of Rochester, who described various approaches to alleviate side effects of drugs. A key aspect is to be aware of what may occur when starting a new drug, reporting problems when they do occur, and having reasonable expectations for the benefits of a drug, as well as its downsides. When people go on anti-Parkinson drugs, what are some reasonable expectations in terms of what symptoms they may alleviate and what they don't attack?

Dr. Irene Richard 2:03

I think that it's important, like you said, to have realistic expectations of what symptoms may improve, and of course, between you and your physician, you have to have an understanding of what medication might be helpful for what symptoms. So the way that we look at this, we might call them dopa-responsive symptoms versus non-dopa-responsive symptoms. So if you think about early on in Parkinson's disease, when you think about what core symptoms are, we usually think of bradykinesia, right? So a slowness of movement. In reality, people will have both slowness as well as a reduction in kind of the amplitude, so the movements will be smaller, as well as a worsening in the rhythm. What happens is that normally dopamine would control our movements such that, you know, they were quick and they were big and they were rhythmic.

The control of movements and dexterity is one of the core features of Parkinson's disease that does respond very well to medications that improve the function of dopamine, as does what we would refer to as rigidity. Even though someone may or may not be aware of the fact that they have rigidity, both rigidity and bradykinesia do respond relatively reliably to medications that improve dopamine. On the other hand, the third core feature of Parkinson's disease that people will often have early on would be a tremor. It's usually a resting tremor present when they're not doing anything, and it tends to go away or improve as you move the limb, but tremor is something that is variably responsive to our dopaminergic medications. So some people will get a good response to a medicine that improves dopamine function, some people will get a modest response, some people will get a little response, and then there are people whose tremor does not respond at all to any of our dopaminergic medications. And if you look at the cardinal features, the two things that get better reliably are bradykinesia and rigidity, and tremor is a little bit hit-or-miss, and you don't know until you try, and you may have to go to higher dosages.

On the other hand, there are symptoms that people will often get with Parkinson's disease that not only don't respond to dopaminergic medications, but, you know, they may not respond to any medications, and those might include problems with balance and problems with thinking or cognition, for example.

Dan Keller 4:41

Let's talk about adverse effects, or as people more commonly call them, side effects of some drugs used for Parkinson's disease. What side effects are often encountered with which kinds of drugs?

Dr. Irene Richard 4:54

In general, the majority of medications that we use to treat the motor symptoms of Parkinson's disease, they increase the function of dopamine, because that's what's missing. Anything that increases the function of dopamine will tell you to improve movement, but it also runs the risk of causing what we call mental status changes, so that could be confusion or visual hallucinations, for example, and really those can happen with any of the medications we use to treat the motor features, although some people are more vulnerable to those side effects than others. In the same vein, certain medications are usually a little bit worse than others, you know, in their ability to cause that.

So if we were going to break down classes of medications, what I would say is that the medication that's the most effective, and that we know has been around for a long time, and is the most likely to help with Parkinson's motor symptoms, that's the medication we often refer to as Sinemet, carbidopa-levodopa. The side effects of Sinemet are actually relatively mild. People can get nauseated, but usually by starting at a very low dose and going up very gradually, we can prevent or minimize nausea. Another side effect might be lightheadedness when you first stand up, and that's caused by a combination of the fact that blood pressure control is disordered in Parkinson's itself, and then the medications like Sinemet kind of contribute to that, so those are kind of the short-term side effects if you were to look at Sinemet, or carbidopa-levodopa.

Another class of medications, which are called the dopamine agonists—basically, the brain receptors think that they're dopamine, even though they're not, and these are medications like pramipexole, the brand name of which was Mirapex, or ropinirole, Requip, rotigotine, or the Neupro patch. So those medications, they can help motor symptoms, not to the extent that the carbidopa-levodopa would, but they can. So the most common side effects that they have that we usually are concerned about: they can be sedating, so they can make people sleepy, and sometimes that sleepiness can come on suddenly. Some people have referred to that as sleep attacks, which is obviously a big concern—for example, somebody's driving and they may not have much warning before they fall asleep. Another is edema, and that's actually swelling of the extremities. It's usually swelling in the legs. In and of itself, that's not particularly dangerous, but it's something of which to be aware, so that you don't go, let's say, pursuing an entire workup with your primary care doctor. They are probably a little more likely than levodopa to cause visual hallucinations in people who are vulnerable to them, and that tends to be people who are older or who have cognitive impairment.

And then the other really important potential side effect of any dopaminergic medication, but probably of most concern, or most commonly with the dopamine agonists, we refer to them as impulse control disorders. And this is basically compulsive behavior, so people can't control their impulse and will do things that they ordinarily wouldn't, or they don't necessarily want to. And the best example I would say is what we refer to as pathological gambling. And there are people who have never gambled in their life, and they start a medication like a dopamine agonist, and they've gambled away their life savings. And when the drugs first came out, we didn't realize that association, and once that association was recognized, it's become really important to let people know about that, because it doesn't seem intuitive that one would think this medication taken for my Parkinson's disease is making me gamble, but the reality is that it can. So we always like to kind of warn people ahead of time just to be aware of it. It doesn't happen to everybody. Younger people tend to be a little bit more prone to it, and certainly people who have either a personal or family history of impulse control disorders or substance abuse, but plenty of people who develop it really don't have a personal or family history of such things. So, you know, that would be with the agonists.

Another medication that we use is called amantadine. It's kind of in a class of its own because it does a lot of different things. It can help the underlying symptoms of Parkinson's. It can also help reduce the dyskinesias that people get over time when they take dopaminergic medications. So those are those wiggly, wriggly movements that people have, and amantadine can not only help the underlying things like the slowness and the tremor, it can also help reduce those dyskinesias. The side effects of amantadine are similar to the others in that it can cause confusion or hallucinations, but in addition, it can cause the swelling in the legs, as well as a rash—a very distinct rash, which is a kind of lacy-patterned reddish rash on the legs. It's not dangerous, but it is something that can be associated with amantadine.

And then probably of most concern with amantadine are what we refer to as anticholinergic effects, and those are basically things that, for the most part, people with Parkinson's already had problems with, so they can get even worse. That would be things such as confusion, problems with, let's say, being forgetful, dry mouth, constipation, difficulty starting to urinate—all of those things, again, we refer to as anticholinergic side effects, and those can be seen with amantadine. In addition, amantadine can sometimes cause insomnia. And the one other thing to keep in mind with amantadine is that, unlike the rest of our medications, it's cleared by the kidneys. If somebody's kidneys, let's say, aren't really working perfectly well—if somebody has some difficulty with their kidney function—the amantadine will build up in their system, so they'll be more prone to getting side effects of it.

Sometimes we add medications, we mix medications. People are aware of that one class of medications called the MAO inhibitors. They decrease the amount of dopamine that's broken down in the brain and therefore increase the available dopamine, and sometimes they're added on to existing medications just to kind of prolong the effect or kind of give a little extra benefit. Those really have similar side effects to the others that I talked about in their potential to cause hallucinations, also their potential to cause a drop in blood pressure when first standing up. And the one other thing I guess I'd mention about them is people will get warnings along with their—when they get their prescription filled, or even the doctor, a warning might pop up as they're making the prescription—that you don't want to combine those with some other medications, including commonly prescribed antidepressant medications. We prescribe antidepressants along with those MAO inhibitors all the time. We just let the pharmacist know, "Yes, we're aware of it," but that's a very commonly prescribed combination.

Dan Keller 12:14

What can be done? Can you just list some things that help to mitigate side effects in general? What sort of approaches do neurologists take to help people overcome them, or even hopefully eliminate them entirely for a certain kind of drug?

Dr. Irene Richard 12:31

The first thing is just, first of all, to establish for sure that it is a side effect of the medicine. It's usually pretty obvious, though not always, so you hopefully only make one change at a time, so you know what's causing what. But if it does appear as though you're having a side effect related to a drug that you started, one thing you might do, it depends, first of all, how serious the side effect is. Right now, for example, if you were to start Sinemet, or carbidopa-levodopa, and you got nauseated, one approach would be, "Okay, let's back down, let's start at an even lower dose, and go up even more slowly to let your body get used to it," because even though you might not have been able to tolerate a rapid increase, maybe if your body gets used to it over time, you will be able to tolerate it, and actually that's usually the case. Another possibility would be that you just have to lower the dosage, you know. Everybody is different.

I would say that one important thing to think about, I'd mentioned to you that all of these medications can potentially contribute to this drop in blood pressure when you first stand up. That's called orthostatic hypotension, and that's actually a pretty big problem, because people stand up and they get dizzy, their blood pressure drops, and all of our medications that I just mentioned, except for amantadine, tend to do that. We will often tell people, "Okay, well, get up slowly, make sure you're well hydrated, you know, add salt to your diet." There are a lot of things that you can do that are, you know, quote conservative measures, but the other really important thing is to look at your other medications that you're on, perhaps for different indications and prescribed by a different doctor.

And the most common ones are medications that you have been taking, for example, for high blood pressure. So you might have had a history of high blood pressure, be on a medication for high blood pressure, and all of a sudden you get Parkinson's disease, and then, particularly when you take a medication to treat it, you no longer need that high blood pressure medicine, and in fact it can worsen that phenomenon of the blood pressure dropping. And another example of a medication that can do the same thing, there are certain medications that are used, particularly in men who have prostate problems and initiating urination—Flomax is an example—that can also cause that orthostatic hypotension. So you kind of have to look and make sure that you're not having an additive effect of two different medications, and in some cases you may just have to make a choice of, you know, well, if you really need the other medication and your conservative measures don't work, you're going to have to either stop the Parkinson's medication or you can treat a side effect with another medication. It's not what we like to do. We don't like to keep adding more medications, but there are medications that we do use, not uncommonly in Parkinson's disease, to increase blood pressure. So depending on the side effect, depending on the medication, there are a lot of different ways that we can kind of deal with the side effects. And the other thing to keep in mind is that there are some side effects that are problematic and, you know, very difficult to just live with, and we really need to do something. There are others that it's important that you notice them, so that you and your doctor are aware that it is a side effect, but it doesn't necessarily mean that you need to do anything differently.

Dan Keller 15:49

I suppose one key to this thing is to be able to work with your neurologist to alert them to these kinds of side effects. Do neurologists typically go beyond just saying, "Are you having any problems?" but saying, "Oh, I see you're on XYZ medication. Have you noticed this, or this, or is it troublesome?"

Dr. Irene Richard 16:10

Yeah, no, that's a good point. What I would say is that generally, when we first prescribe a medication, we would tell people what the most common side effects are, what to look for. I mean, obviously, if you read a package insert, it contains everything, so we don't recommend doing that. But, for example, with Sinemet, when we prescribe that, I'll say, "You know, it can cause a little bit of nausea, that's why we go slow. It can cause some lightheadedness when you first stand up, that's why you want to take your time." On the other hand, with dopamine agonists, it's really important that we warn people about the potential for both the swelling in the extremities, as well as this impulse control disorder, because people might never put the two together or even think that it's a side effect.

So, from a patient standpoint, it's always helpful to ask what potential side effects might I experience with this medication, and it's important from the neurologist's aspect to be proactive and tell patients, you know, you don't want to scare somebody, but you can tell them, "Not everybody gets this, but potential side effects, ones that we see commonly, are X, Y, and Z." I'd say that most neurologists tend to be rather conservative, and that we'll start at a low dose and we'll go up gradually. It depends upon the neurologist, but we'll tell people, "Why don't you contact us when you get to, let's say, a certain dosage, and then we'll let you know what to do from there." In other words, let's say you get up to one tablet three times a day of Sinemet. If that's worked and you're tolerating it, you could just stay there. On the other hand, if you're tolerating it, but it didn't really help, let's say your dexterity or tremor, we may then say, "Okay, let's keep going up, then."

But it's certainly important if you are on a medication, particularly if it's a recently started medication, and you are experiencing a new symptom, it's important to bring it up to your neurologist, first of all, just so that they know, and they may be able to determine, "Oh, well, you know, that might be a side effect of this medication." Or even if it's not a side effect of this medication, maybe it means something else is going on that you should get checked out with your primary care doctor. So I do think that kind of being vigilant, but not necessarily overly vigilant, but on the other hand, if you're having symptoms, particularly something new, it never hurts to mention it.

Dan Keller 18:28

Is there any sort of nutshell message to patients and caregivers in this area?

Dr. Irene Richard 18:35

I think that when it comes to Parkinson's disease, it's good to understand that the medications we use are used to treat symptoms. I think one thing that I really do want to get out there, because I think it's such a prominent misconception, still seems as though people are very hesitant to start Sinemet or carbidopa-levodopa. We've really come full circle, and over the past 10 to 15 years, we've done extensive studies, and while it was a concern initially about should we wait to start Sinemet, the majority of Parkinson's disease specialists have come to the conclusion that when somebody is having symptoms that would respond to Sinemet and those symptoms are impacting their quality of life, they should not hesitate to start carbidopa-levodopa, because it will improve their function, it will improve their quality of life.

And even things like exercising that we know is really helpful for Parkinson's disease—in fact, it may even slow the disease progression, we know it slows emotional decline—Sinemet, for example, carbidopa-levodopa, there's even been studies that show that people get more out of their exercise when they're on Sinemet than when they're not. And eventually, because everybody ends up being on Sinemet for the most part, what you have to do is look a few years down the road and realize everybody looks the same at that point. In other words, it doesn't change the ultimate outcome, but what it does do is, if you start it sooner, in retrospect, you probably will have had better functioning, better quality of life for a longer period of time than had you tried to hold out on starting it. That's just one real important message, because I just see it so commonly.

Dan Keller 20:16

Very good, I appreciate it. I'm sure this will all be helpful to our listeners. Thanks.

And for more information on side effects of drugs used to treat PD, there's a wealth of information on our website at parkinson.org. Just search for side effects. While a lot of the information relates specifically to PD drugs, much of the advice and therapeutic approaches are also useful regarding medications for many other conditions, since side effects of drugs are a very common phenomenon. An expert briefing by Hubert Fernandez gives a good overview of many of the drugs used to treat PD, their efficacy, and side effects. Several fact sheets, along with our medications publication, cover many of the classes of drugs, including levodopa-carbidopa, dopamine agonists, amantadine, monoamine oxidase inhibitors, and more. You can find these materials in our PD library at parkinson.org/library.

You may also find useful information by revisiting past podcasts in this series. Emily Perrin covered pharmacy challenges in PD in a podcast with that title. In back-to-back episodes, Joe Quinn and Martha Nance discussed hallucinations. One podcast is titled Do You See What I See: Hallucinations and Parkinson's Disease, and the other is Hallucinations and Delusions in Parkinson's. Mark Groves addressed impulse control disorders in an episode called Clinical Issues Behind Impulse Control Disorders, and coming up in 2021 we will feature Greg Pontone giving some tips on how to talk to your doctor about impulse control disorders.

If you have questions about today's topic, or anything else having to do with Parkinson's, our information specialists can provide answers in English or Spanish. You can reach them at 1-800-4PD-INFO. News and updates about future events and resources are available by joining our email list at the bottom of our website's homepage. If you want to leave feedback on this podcast or any other subject, you can do it at parkinson.org/feedback. If you enjoyed this podcast, be sure to subscribe and rate and review the series on Apple Podcasts or wherever you get your podcasts.

At the Parkinson's Foundation, our mission is to help every person diagnosed with Parkinson's live the best possible life today. To that end, we'll be bringing you a new episode in this podcast series every other week. Until then, for more information and resources, visit parkinson.org or call our toll-free helpline at 1-800-4PD-INFO, that's 1-800-473-4636. Thank you for listening.

Dr. Irene H. Richard is Professor of Neurology with a secondary appointment in Psychiatry at the University of Rochester in Rochester, NY. After graduating from Cornell University in 1987 and receiving her MD from the Yale University School of Medicine in1991, Dr. Richard completed neurology residency at the University of Rochester in 1995, followed by NIH-funded fellowship training in Movement Disorders and Experimental Therapeutics in 1997. She joined the faculty of the University of Rochester in 1997. Dr. Richard provides neurological care for patients with Parkinson’s disease and related conditions and her clinical research program focuses on the psychiatric aspects of these illnesses. More recently, with support from a PF community grant, she has also been working with her team on efforts to optimize hospitalization for patients with PD. She is the author of numerous scientific articles, editorials and book chapters and has been the recipient of career development awards and research grants, including one from NIH to lead a multi-center clinical trial evaluating the treatment of depression in PD. Dr. Richard is Director of the University of Rochester’s Movement Disorders Fellowship Program, LBDA Research Center of Excellence, and Parkinson Foundation Center of Excellence.

Dan Keller 0:02 Welcome to this episode of Substantial Matters: Life and Science of Parkinson's. I'm your host, Dan Keller. At the Parkinson's Foundation. We want all people with Parkinson's and their families to get the care and support they need. Better care starts with better research and leads to better lives. In this podcast series, we highlight the fruits of that research, the treatments and techniques that can help you live a better life now, as well as research that can bring a better tomorrow. Shouldn't everyone have access to good medical care, regardless of their gender, race, community, or financial situation? These factors and more can be barriers to accessing health services, creating disparities in care and ultimately in quality and possibly length of life, but another impediment may be just knowing what one needs, what is available, and how to access it.

Dr. Lynda Nwabuobi, a movement disorder specialist at Weill Cornell Medicine in New York, recognized this problem in the communities surrounding Columbia University, where she received her training in movement disorders under a Parkinson's Foundation Movement Disorders Fellowship Award. She recognized that if people did not know about the healthcare services available, they would not be coming to her clinic, so she decided to reach out to underserved communities where they gathered to set the stage. I first asked her if the fellowship award furthered her research on health disparities.

Dr. Lynda Nwabuobi 1:51 Absolutely, I felt very lucky to get this award and to be able to attend Columbia University to do my movement disorders fellowship. I think one of the best things about being there and getting this award is that I was able to get trained at a Parkinson's Foundation Center of Excellence, and I was able to get excellent education. I was around some of the brightest minds in Parkinson's disease, like Dr. Stanley Fahn, and also being a Center of Excellence, it really taught me what it's like to be part of an interdisciplinary team, which is really important in Parkinson's disease care, and that's basically having a team of people, like, you know, like a social worker, a nurse practitioner, speech therapy, physical therapy, the ENT doctor, geriatrician, neurosurgeon, whatever you may have it, and having this group of people who just surround you and all work together to provide you excellent care. So I really learned what it's like to be a part of a team like that, and then also being at that center helped me realize a lot of collaborations with other Parkinson's disease community organizations, and really helped me further my career. I think that's one of the amazing things I was able to get from doing that fellowship.

Dan Keller 2:58 What made you interested in Parkinson's disease, to begin with?

Dr. Lynda Nwabuobi 3:03 One of the things that drew me to Parkinson's was, I think, the physicality of the disease. As a neurologist, we truly rely on our physical exam to diagnose a lot of neurological conditions, so being able to detect the cardinal features of Parkinson's at the bedside and feel fairly confident in the diagnosis, I really found that appealing. And I remember as a resident walking into a room in the hospital and quickly recognizing that a patient that I was about to see had some Parkinson's disease features. I found that so fascinating. That was the first thing that drew me to it.

And then, secondly, Parkinson's disease, it typically affects an elderly and geriatric population, which I think is a group of people that are often ignored in our society, and I do have a passion for taking care of individuals in this age group and providing them care, just for the simple fact that I feel that they are often ignored, and life shouldn't just stop because you're 65 and above, I really don't believe in that.

And finally, sometimes about Parkinson's, it's a disease that truly affects the whole body and the whole being, and it's a chronic disease that you live with for the rest of your life, and it necessitates getting additional support from friends, family, really anywhere that you can, and I find that I enjoy, you know, managing the patient, the family, and just like coming together with a group of people, all hands on deck, to provide the best care for the patient. So all these things about Parkinson's disease and the way it affects the patient, it truly drew me to it, and I made it the focus of my career.

Dan Keller 4:31 Age is just one disparity that people encounter in getting health care. So, what inspired your work in investigating health disparities, specifically in Parkinson's disease?

Dr. Lynda Nwabuobi 4:45 Many disparities have been found in Parkinson's disease care. There are several papers that have been published on it, and for example, one of the biggest ones that we know is that individuals who identify as African American or Hispanic, they're 30 to 40% less likely to get neurologist care, and they're more likely to be seen in their communities by non-neurologists, you know, primary care physicians, or other providers. We've also seen other findings that, you know, people who are African American, they're less likely to be initiated on Parkinson's disease medications at diagnosis. We also know that women are less likely to be taken care of by a neurologist or Parkinson's disease doctor, so these are some disparities, just a few among the many that have been found.

And in my work, one of the first pieces of research that I did that focused on Parkinson's, I looked at individuals who were homebound with Parkinson's disease, and I looked to see what are some of the social factors and any disparities between the men and women in this group, and the main finding from that was that women are more likely to be alone. The women with Parkinson's who were homebound, they're more likely to be alone, and this has been reported in other studies. And I think that lets us know that this is a group of people that we need to pay more attention to, because we know that Parkinson's requires a lot of support beyond the individual with the disease. So that's one of the first things I looked at.

And something else I've looked at is comparing the care between two groups of Parkinson's disease individuals that were cared for, one group at a public institution, a public hospital, and then another group at a Parkinson's Center of Excellence, and both groups were cared for by the same physician, and the goal was to see, is there any difference in the care that both groups received. And one of the findings was that the group being taken care of at the public hospital, they had less access to physical therapy and exercise, and they faced more barriers to getting deep brain stimulation, and they also had less access to one of the major Parkinson's disease medications that we use, and these are all differences, despite having the same treating doctor, and this tells us what institution you're located at can affect the type of care that you get. So, these are some disparities that I also found in my study, and I think the goal is for us to be more aware of these disparities and find solutions to tackle them.

Dan Keller 6:56 Another thing that affects the kind of care you get is what comes out of research, those findings. Is there a disparity in who participates in clinical trials, that they are represented, and you know what happens, how they react, and how they should be treated?

Dr. Lynda Nwabuobi 7:13 Absolutely. You know, when you look at the majority of Parkinson's disease studies, and you look at clinical trials, the majority of patients who participate in these trials are people who identify as white ethnically, and then it's also a majority of men. I don't remember the number exactly, but it's a very high percentage—over 90% of these studies have white patients as the majority of people who are participating in these studies, and so it affects the results that we get from these studies, because we're not able to generalize them to the whole population. And so the results that we're getting from testing new drugs and treatments on a majority of individuals that are white, we're also applying the same treatments to individuals who identify as ethnically Black or Hispanic or Asian, and so we need to have more diversity in clinical trials, making sure that the clinical trials reflect the individuals that are treated, but this is a definite fact that has been seen in many studies.

Dan Keller 8:11 So, what are some of the potential solutions on how you can start providing more equitable care for Parkinson's disease patients, especially for some of the communities you identified who are getting suboptimal recognition?

Dr. Lynda Nwabuobi 8:25 I think one of the first steps is just being aware, so having conversations like this, like we are having right now, people being aware that these disparities do exist, because if you don't hear about it, you assume that everyone is getting equal care, right? So being aware that they exist and knowing what the disparities are, and then to providers and clinicians and organizations who are involved in Parkinson's disease, I think finding gaps in the care or services that you're providing to make sure that you are reaching every single person appropriately and that you're providing optimal care to them.

So, for example, if you find that you're not prescribing a certain medication to a certain group of people, finding ways to address that. For example, at the institution where I did my research, one of the Parkinson's medications was not on the pharmacy formulary, and that was just one barrier. And so this would involve having conversations with the pharmacy to see how we can include that medication into the hospital formulary.

And then something else that we see often is academic institutions oftentimes have separate clinics for their private patients versus their Medicaid patients. I think that's a bigger issue in our healthcare system, and in this country, but I think that when you have a system like that, that can easily lead to disparities in care when you have two separate clinics treating people based on, you know, their insurance status, right? And I think ensuring that you look at both groups, both clinics, and make sure that you're providing equitable care for both groups.

And then also, Parkinson's disease organizations—there are a lot of big, powerful ones in our country. I think that one thing they need to do is to ensure that their outreach efforts are culturally sensitive, meaning that they are considering the cultures, beliefs, the history, and language of marginalized groups when they create their programs, and making sure that there's translation to other languages, so that everyone can truly access it. And then making sure that their programs are not just online, because not everyone has access to a computer or to the internet, but making sure that they actually physically go into communities to reach people who are not able to access things online. And I think one easy way to do that is, you know, partnering with community organizations that are already doing the groundwork, located in these communities and are trusted, partnering with those organizations to find a way to reach these communities that we're not being able to reach, so far.

I think, you know, for our individuals with Parkinson's who may be listening to this, you know, you may be wondering, how can you help? I think that Parkinson's disease, it's a disease where we have a lot of advocates in our patients and their family members, you know, people go and do the walks and the runs for Parkinson's disease every year, so we have a lot of advocates, and I think being more aware about these disparities now, you can serve as an advocate for them. For example, your institution that you get your Parkinson's disease care at, you know, asking them, "What are you guys doing about these health disparities that you know I just learned about? Are you guys making sure that you're reaching out to, you know, different patient populations and marginalized communities?" I think patients can advocate by reaching out to Parkinson's disease organizations, and you know, asking them these same questions. How are you tackling this issue, and just holding institutions and organizations more accountable?

Dan Keller 11:36 One way you did outreach, I saw, was you went to a local farmer's market, yeah.

Dr. Lynda Nwabuobi 11:42 Yeah.

Dan Keller 11:42 And what was the reaction, and what was the outcome? Did people actually become more aware and take advantage of certain things that the health system could offer? Yeah.

Dr. Lynda Nwabuobi 11:51 Yeah, that's a really good question. So I started this farmer's market effort, I call it Neurology at the Market. I started this while I was a Parkinson's Foundation fellow at Columbia, and one of the things I noticed at our institution, when we see the majority of our patients, is that the majority identified as white. However, we were located in a neighborhood that's very diverse, predominantly Hispanic, and so there was a mismatch, and I felt that we needed to go more into the community that we're surrounded by and teach them about Parkinson's, learn what they know and make sure that they know that we are here at this institution. This is what we treat.

And so the farmer's market, I walked by it, you know, every week, and I thought that would be a great idea to set up a table there, and it would be a good way to reach the community, because when you look at the market, it's the perfect demographic for Parkinson's, you know, people from the community come in and shop, and it was just a very comfortable environment. So I partnered with the institution, some organizations, and GrowNYC markets, and set up a table there, doing it once a month over the summer. I believe it was 2019 I started this, and I had our social worker with us who spoke in Spanish, and we basically just provided a lot of information about Parkinson's to people who came to shop at the market.

People really appreciated basically kind of getting an insight into Parkinson's, and just having a doctor who's standing there and just chatting with you about different things. And we also collected a survey, and you know, we found that people knew mainly about tremor, but did not know about the other symptoms of Parkinson's. We met some people who were affected by Parkinson's in different ways in their lives, and you know, they learned more about our clinic. I wasn't able to measure whether the market itself was able to bring in more patients to our clinic, but it showed that, you know, doing the market is a feasible way to be out there and do community outreach and have people learn more about Parkinson's.

Repeated the market just last year, and this time I have, you know, medical students, residents involved, and we're teaching not just about Parkinson's, but about other neurological ailments, because I got the sense based on surveys that people wanted to also learn about other things: headaches, migraines, stroke, dementia. And so we've incorporated education also into the plan, but Parkinson's remains the main focus, and it's been quite successful being able to reach different people from different walks of life.

Dan Keller 14:06 I noticed in a picture of you at the farmers market you were wearing a white coat, which is often taken as a sign of authority and credibility, but does that also create some distance from the crowd you're trying to reach or relate to? What's your recommendation?

Dr. Lynda Nwabuobi 14:22 That's an excellent question. And so the first day I did the market, I wore my white coat. The first time I went into the market, I noticed that people did not notice me. They saw the table, they saw materials, and would just come look at it, but they did not see me as a person who had the knowledge that they could benefit from, even though I was wearing my ID. And so I think that once they saw the white coat, they go, "Oh, you're a doctor," and once they recognized that, I noticed a shift that people felt more comfortable asking me questions and feeling like they could learn something from me.

However, when I repeated the market last year, I wore a jacket from my institution, not a white coat, because I had other people with me, like our residents and our medical students, and I didn't want everyone wearing a white coat, because that would be too much, and I just told everyone, wear your ID, wear, you know, something that has our institution logo on it, and present ourselves that way. But I think it can go both ways. It depends on the community. I think in that community, where I was for the first market, they took me more seriously because I was wearing a white coat, and I did see the difference.

Dan Keller 15:22 Have you thought of other places or venues to do outreach similar to what you were doing at the farmers market, but if people don't have them around their area, what kind of places should they look for?

Dr. Lynda Nwabuobi 15:34 I think partnering with community centers, senior centers, they are able to draw the perfect demographics that are often affected by Parkinson's. The senior centers, different seniors in the community come and, you know, have their lunch, they play games, and so, you know, you can sneak in a lecture in there, a quick 30-minute lecture and a talk, and teach people about Parkinson's. So that's one really good place to reach out to individuals, and religious organizations, such as churches. I've worked with churches in the past to do some education, so religious organizations are another one. And then, you know, just other community organizations that do government work, are in the community, and provide different resources to the community and are trusted. Those other community centers also, you can give talks to them.

And then I'm doing a lot of my talks these days via Zoom with the pandemic, and this way I found that I've been able to reach even more individuals, you know, by working with these different organizations and setting up different Zoom talks. One of the talks that we do here at my institution at Cornell, it's a video conferencing talk, and it's basically broadcast to different senior centers all over New York City, so to the other boroughs, so Brooklyn, Queens, the Bronx, and so I'm giving this talk, but it's reaching to different senior centers and different organizations and different people in their homes at the same time, so I found that doing talks over Zoom actually I've been able to reach more people.

Dan Keller 17:00 Well, this has been great. It puts a whole new perspective on reaching communities and informing them and finding out what they know, what they're thinking, and maybe even misconceptions. So, thanks a lot.

To see more about Linda's training and her research on healthcare disparities, search our website at parkinson.org on disparities and see the article called Tackling Disparities in PD Care: A Movement Disorder Specialist's Story. Also, within that article is a link to information about the Parkinson's Foundation Movement Disorders Fellowship, and how it has supported specialized training for more than 40 years. As the number of people with PD is expected to increase as the population ages, the need for this sort of training and care is greater than ever.

In terms of healthcare disparities and underserved communities, you may be interested in some past podcasts. One with Aaron Daly is called Extending the Reach of Parkinson's Resources and Services. In another called PD Across Race and Ethnicity: Outreach to the African American Community, Dr. Reversa Joseph describes her efforts to raise awareness of PD in this community. For an international perspective, see Parkinson's Education Through Community Outreach, in which Dr. Louis Tan describes an outreach program in Singapore and the alliances they have made with other organizations. To hear about gender disparities in PD research and care, go to the podcast called Encountering Gender Differences: Women and PD Initiative. Along these same lines is a podcast titled Addressing the Unmet Needs of Women with PD, and remember that we have several bonus episodes in Spanish.

As always, our helpline information specialists are available to answer questions in English or Spanish about today's topic, or anything else having to do with Parkinson's. News and updates about future events and resources are available by joining our email list at the bottom of our website's homepage. If you want to leave feedback on this podcast or any other subject, you can do it at parkinson.org/feedback. If you enjoyed this podcast, be sure to subscribe and rate and review the series on Apple Podcasts, or wherever you get your podcasts.

At the Parkinson's Foundation, our mission is to help every person diagnosed with Parkinson's live the best possible life today. To that end, we'll be bringing you a new episode in this podcast series every other week till next time. For more information and resources, visit parkinson.org or call our toll-free helpline at 1-800-4PD-INFO. That's 1-800-473-4636. Thank you for listening.

Dr. Lynda Nwabuobi is an Assistant Professor of Clinical Neurology at New York Presbyterian/Weill Cornell Medical College, specializing in Parkinson’s Disease and Movement Disorders. She diagnoses and treats individuals with Parkinson’s disease, tremors, and other neurological diseases with the goal of delivering compassionate and individualized care using a holistic and multidisciplinary approach. In addition to her clinical work, Dr. Nwabuobi has interests in health disparities research, teaching, advocacy, and community engagement/outreach. She has published several articles in peer-reviewed journals and has presented her work at national and international conferences. She is actively involved in educating underserved communities about Parkinson’s disease with the goal of improving access to care.

Dr. James Beck 00:00:00
Hello everyone, and welcome to the Parkinson's Foundation Expert Briefing. I'm Dr. James Beck, chief scientific officer of the Parkinson's Foundation, your host for today, and it's a pleasure to have you with us. In today's briefing, we'll explore how artificial intelligence, or AI (something maybe no one's ever really heard about), and these AI-enabled tools may influence communication, symptom tracking, care personalization, and how individuals and care partners can engage with these tools responsibly. But before we begin, I always like to get a sense of who's joining us today.

We're going to launch a poll. In this poll, if you're joining from Facebook Live, you can respond using the comment section as part of the process. So, in this poll, you'll see what we're really trying to do is get an understanding of your connection to Parkinson's disease. Are you a person with Parkinson's? Are you a spouse, care partner, family member? What's your relationship? This helps us really understand who's joining us today and can allow us to tailor our presentation on the fly.

I'm expecting a lot of people with Parkinson's joining, but sometimes we get surprises with a lot of scientists and researchers as part of the process. So, let's see what we get as part of that process. Hopefully people have gotten a chance to respond. You can do it on your phone or, if you're sitting in front of an old-fashioned computer like I am, use the mouse to click the right spot as part of the process. Let's give it another minute. Okay. Yeah. Great. A lot of people with Parkinson's joining us today, as well as some other health professionals and some scientists and researchers.

Great. Wonderful to see. Thank you for sharing your connection to Parkinson's with us. Before we begin to get into the meat of our discussion today, I always like to take a moment to introduce the Parkinson's Foundation. We are a nonprofit organization dedicated to improving the lives of those living with Parkinson's by enhancing care and advancing research.

Our efforts are deeply rooted in the collaboration with the Parkinson's community and ensuring that everything we do aligns with your needs and priorities. Today's program is an example of how we're working with you to meet those goals.

For those of my colleagues, we've got two slides presented right here, so we just need to eliminate one screen. There we go. One of our key initiatives is part of this, you know, relies upon feedback and engagement of the community, through investing in breakthroughs. PD GENEration is, I think, top of that list. Here with PD GENEration, we offer free genetic testing and counseling to people with Parkinson's.

By participating in research, you can learn about your genetic connection to Parkinson's disease and contribute to the research effort that drives new treatments and, hopefully, one day a cure. So we encourage everyone to share this opportunity with your community. We know a lot of people in our Parkinson's Foundation family have already participated, so thank you very much for that. I think the key thing here is that together, as always, we can make a difference.

Dr. James Beck 00:03:56
And so what I'd also like to do next, and I think it's very relevant for today's topic, is introduce you to Ask PAM. This is the Parkinson's Foundation's latest tool, the Parkinson's Assistance Messenger. PAM is an AI-powered chat tool from the Parkinson's Foundation that provides trusted, evidence-based answers about Parkinson's disease. You can ask PAM anytime, anywhere. The key thing about PAM is it's secure, it's confidential.

As always, it was created to give people with Parkinson's and their caregivers instant access to accurate Parkinson's Foundation-rooted information 24 hours a day, seven days a week. The information that comes from this chatbot is all derived from stuff that we have created and vetted. It doesn't go out beyond the Foundation's network.

And so you can really feel confident that the information you're getting is something that's accurate. Like all the information that we provide on our website, this is really educational information. It's never meant to replace medical advice from your healthcare provider. Use it in that way, of course.

One thing we always do with our Expert Briefings is record them. This is no different here. So if you are having to step away, or you come in midway, or you know people who want to watch this, there will be a recording of this. If you've signed up, you'll get a link to when that recording is ready, and then at that point, you can share that with others as part of the process.

Now I'd like to take an opportunity to introduce our speaker for today, Dr. Allan Wu. Dr. Wu is a professor of neurology at Northwestern Medicine's Parkinson's Disease and Movement Disorders Clinic. He's worked as a physician informaticist for over a decade on electronic health record projects with the specific goal of looking to improve patient engagement, reduce physician burnout, and enhance efficiency. He collaborates with the Parkinson's Foundation today and the electronic health record provider Epic on a national initiative to improve hospital safety for those living with Parkinson's.

This work has led to the integration of PD-specific safety features into Epic's EHR system, helping ensure that people with PD receive safe and consistent care wherever they're hospitalized. Boy, I'm a tongue twister today for me. Dr. Wu, thank you for joining us today, and I look forward to your talk.

Dr. Allan Wu 00:05:17
Thank you, Jim. I'm going to go ahead and share my screen and get going. That was a great intro, and a privilege to be here. All right, great.

Thank you. My topic is artificial intelligence and Parkinson's: understanding promise and pitfalls. I'm going to get right into it. There's a lot to cover. My disclosures: I actually don't have any financial disclosures with anything that I'm going to be talking about. I have volunteer disclosures that Jim touched upon. I volunteered at the American Academy of Neurology. I'm chair of the Practice Management and Technology Subcommittee and a member of the Medical Economics and Practice Committee, and I'm also a member of the Brain & Life editorial board, which is a free publication online for people with neurological conditions and their caregivers.

Also, I am a volunteer elected member of twelve other neurologists around the country on Epic, which is a major electronic health record vendor steering board, which helps put neurology-specific content into that electronic health record vendor.

We're going to go through three parts. The first part is a basic understanding of AI, defining what it is and how it works at a very high level. Again, people on this call probably have differing experiences with AI. So those of you who are experienced, you'll have to put up with this part. And for those of you who don't quite know and are curious about AI, maybe you'll find something here to level set. Then I'll go through some examples of AI, broadly speaking, in action, real-world examples that are relevant to our community, and then some basic tips on how to use AI, particularly the large language models, safely and responsibly.

But first, a little bit about me and why I'm here, perhaps. First of all, I do not consider myself an expert in AI. As will become plain, I use parts of AI and have some experience with using it, but what I really am, as in my introduction, I am a physician informaticist. I'm a clinical informaticist. I will define that more in the next slide. But I've been involved with this world since 2013, and that's a long time ago, and that predates the modern era of AI.

Dr. Allan Wu 00:07:43
There's a slide there, a picture from 2014, where there was a group of us mid-career physicians at UCLA that formed a group that advocated for health information technology during a transition to electronic health records, which were promised to solve all of our healthcare problems, and guess what? It did not. Therefore, there became a retained need for physician expertise in health information technology and making it work for us and not against us.

There became, in 2013, 2014, bottom left, the American Board of Preventive Medicine has now certified a physician specialty in clinical informatics. I am the program director of that at Northwestern now, and we train physicians to become stewards of electronic health records, health information technology, at the level of helping individuals. That is physicians, that is patients, that is caregivers, and that is our staff one-on-one with health information technology.

The things on the right I already mentioned in my disclosures, but I did want to give you this idea that I've been working with health information technology for a while. Clinical informatics is not just about the technology. It is about the people who are using it. It's about the process of making sure that it's integrated into workflow. These pictures and headlines are from my other talks on clinical informatics, and they are talking about how we need to make sure that the little picture on the left is showing the busy doctor on the left, typing busily on their computer, on their chair, back against the very happy kid on the exam table and their parents.

It was a commentary on how we have become, we are typing on computers more than talking to our patients. The promise was that electronic health records would improve our care and solve a lot of problems. It did not. AI is promising the same thing, and in my view, I think it's still about people, process, and technology. I do not think AI is going to solve all our problems, and it's still going to require people, advocates, physicians, and physician informaticists to extract the value out of them.

Dr. Allan Wu 00:10:20
This is a picture of what's called the fundamental theorem of clinical informatics. This is from 2013, and it was the premise that a human plus health information technology should be better than the human. That is a driving theme and should be the theme as we move into a new era of technology, including AI. And what defines better? This is the classic kind of triple aim of healthcare: improving the patient experience, lowering the cost of care, and improving the health of the population. There are some that will advocate for a fourth aim, which is reduced clinician burnout, because if we have physicians retiring in droves, that does not help our efforts either.

But this is background about where I am coming from into this world. With that, I'm going to go into the first part, which is the AI 101 understanding AI. There is hype about AI and a promise, and these are quotes of very, very optimistic, overly so, people.

Demis Hassabis there on the left is a computer scientist, but he won the Nobel Prize for using AI to show how proteins could fold. He's claiming that maybe we'll cure all disease with the help of AI in the next ten years. And on the right side is a prominent essay from futurist Dario Amodei, who said, I think AI could increase the rate of our discoveries by ten times. I think this is more in the mark of trying to enhance our abilities with AI technology, giving us all this progress.

Of course, you get these headlines. The first one, from a pilot at Sutter Health, saying, you know, with ambient AI, which is AI listening to the conversation and writing clinic notes for the doctor, doctors can give patients their full attention. There's reasonably good evidence that that is actually helping occur. And then, of course, HCA Healthcare System was prominent in trying to leverage AI to focus on improving safety in their hospitals.

Dr. Allan Wu 00:12:44
Those are the hype and the promise, but there are at the same time these concerns that the ambient AI is going to weaken your relationship with patients. That's from the AAMC, representing medical schools and colleges. AI chatbots provide poor answers to medical questions half the time. If a doctor was providing you poor answers half the time, I hope that doctor is not going to continue getting new patients.

And of course, Americans, and again, I understand there is a very broad audience here, international included. Some of the data I'm going to show in the polls tend to be based in the United States. There was a poll that basically showed that Americans are very concerned about AI, with one of the major concerns being the misleading video and audio deepfakes. You can see Sanjay Gupta of CNN over there basically calling out that there was an ad going around saying that he discovered how Alzheimer's could be reversed with a traditional simple Indian recipe, and he's saying, you know, that's not me, and this can only get more realistic over time. So there are concerns about this technology.

The reality is, just like electronic health records are in our medical clinics, it is going to be hard in most of the world to find clinical care without some electronic health records. It's harder to avoid AI in some form. Americans use AI, in this other survey broadly defining AI-enabled products, you see there, but basically 99% of us touch something: a website, a shopping site, our smartphone, our Amazon lists. These things are all included in that category. So it's here in various forms.

Generative AI, which is the new era of creating AI that is creating novel outputs, this is the chatbots and so on. Over half of people have used it for personal purposes, 28% at least weekly, and still about a third, 31%, never used it. The skepticism in this poll, which was March 2025, so between 2024 and 2025, it's already a year old, there was a sign of growing skepticism, with the feelings of being overwhelmed, skeptical, you can read it there, curious is a good one, cautious, concerned. And there was a bit of a shift downward in being impressed, hopeful, or excited.

Dr. Allan Wu 00:15:26
With that, I'm going to go into a little bit more about the 101 of artificial intelligence. There's a lot of definitions that get pretty technical, but basically I try to simplify it. It's basically computers doing things that, if a person did it, you would think they require intelligence. And I can hear you all thinking, yeah, yeah, there are things that we do, that humans do, that do not seem to require intelligence. But in the end, basically, computers are doing things, responding, writing back texts, talking back to you, and they are things that you would not have expected computers to be doing, say, just a decade ago.

So that's a very broad definition of artificial intelligence, and it comes in a spectrum. We're going to come back to this a little bit more, but basically, coming on the left, they are very, very simple rule-based types of assistance that computers can do. Rule-based is like following a flow chart. It's like an if-then statement. Flow charts have inputs, like if your cholesterol is high, get on an anti-cholesterol medication. You know, very deterministic. If the input is known, you have the predictable output. You can explain it. Any human can read it, understand it, and at most, flow charts are, what, one to five decisions deep.

Statistical models get into about ten to a hundred different parameters, right? That's like how many things do you need to put into your statistical model to predict how, you know, a table for life insurance, for example. What information do you need? It's not going to be ten things. It might be fifty things. Machine learning increases that again into the hundreds to hundreds of thousands of parameters.

Dr. Allan Wu 00:17:13
Machine learning is taking patterns of things and learning from those patterns. This is a workhorse that you'll learn about what AI is doing in the medical field, and it's like your smart watch detecting AFib, right? It's finding patterns, but hundreds, a hundred thousand parameters in that.

Deep learning is kind of like machine learning, but different specialists are in there. So it's like processing a Face ID, where one machine learning thing detects the eyes, another the mouth, the other the face, and so on and so forth. And so that gets into the millions of parameters. Then generative AI blows it all out of the water. It now is in the billions of parameters, with rumors of the newest models having trillions of parameters. It boggles the mind. So what that means is the outputs are no longer explainable.

There is no way, like we can understand a statistical model. If you are older and have more comorbidities, diseases, you might have a shorter lifespan than somebody who is younger and healthier. But in generative AI, there is no way to even predict what's going to come out of that thing. More bottom-up, emergent, you know, quote knowledge. You can't predict what's going to happen. It looks novel, and it's been trained on lots of things. But you can sort of see the development of this from one side to the next.

Then I want to talk about this concept. This is from the American Medical Association, and I do think this is a very, very good framing. It's called augmented intelligence, which is basically defined as artificial intelligence that focuses on the assistive role of AI. Its design and its use should enhance human intelligence rather than replacing it. This basically is a reframing of the 2013 fundamental rule, fundamental theorem of clinical informatics: let the health information technology enhance and assist us.

And I think this is some theme that you should hold on to as we explore AI and as you explore AI. Again, AI 101, we unfortunately have to go through more definitions. You might come across these. I don't think we need to dwell on this for the purpose of the talk. But you might come across these things in your reading, and in media and lay materials.

Specific AI is AI, any of the types that I showed, that is trained to do one specific task, like this AI tool transcribes a doctor visit into a clinic note. That's all it does. It doesn't do things like plan your vacation, right?

But a general AI could do things on novel problems that it is not trained on. So the example here is: what did my doctor mean by dyskinesia? You can say, explain it to me in Spanish, or explain it to me at a sixth-grade level, or give me dyskinesia in the context of all the movement disorders that could exist. It'll answer things beyond what it's been trained on.

Generative AI actually will combine that into more novel output, where it will summarize things and create a new kind of information with follow-up questions. And the agentic AI are basically AI agents that are empowered, meaning you gave them permission, if you are in control of that agent, to take actions. So you can give it permission to go to your computer, reorganize your files, clean up your inbox and your email.

Then the example there on the side is, you know, this is sort of a more future approaching, but it reads your visit summary. It will then reach out to your pharmacy, request a med refill, submit a prior auth for you, schedule a follow-up with you, and keep calling the clinic for you on your behalf over and over again until a cancellation opens up and you can get your appointment, that kind of thing.

So, just to put a pin in it, and we can go through this quickly, rule-based are things like spell check and appointment reminders. Is the word spelled right or not? Is the appointment today? And if it is, send a reminder, right? Very rule-based, easy to understand.

Probability models, those are your insurance risk calculators. Your weather forecasts are probability models. It changes as time goes on, likelihood. Appointment cancellation is an example of application in clinic operations.

Now machine learning is those Amazon, Netflix show recommendations, a very fancy text completion that happens automatically in some emails, and patterns in your wearables and your Oura ring or your smartwatch. Deep learning is more deep, multi-large data extraction tasks like dictation, speech recognition, voice-to-text, Face ID. Photo sorting by some of the photo apps will now sort by face. That would be deep learning.

And then generative AI, we will talk about ChatGPT, is the prototype that came out in 2022. Then there's these other ones, Claude, Copilot and chatbots. Now you've got PAM at the Parkinson's Foundation that is a generative AI product as well.

Dr. Allan Wu 00:22:35
AI can mean any of those things, and it can be more than one of those things. So, if you just take the example of the AI, ambient AI in the office visit, a microphone is listening to the physician, the patient, the family and the conversation. That's deep learning. Deep learning is looking for patterns in the audio waveform pattern of what it's hearing, accent or not, and it will process that and turn it into text that a computer could compute.

Then generative AI will take that information and combine it and summarize it into a clinical note. Then a physician is responsible for reviewing that note and approving that it is their responsibility, and that would be the augmented intelligence part of things. But, you know, we will say ambient AI and we mean all of this.

Okay. Large language models. So this is the most accessible part of the current era of generative AI. You can see, I tried to figure out a way to represent how popular it was. This is a Google Trends search that I did using the term ChatGPT, which basically doesn't exist until the end of 2022, and then it starts taking off. It's like one of the most popular. People signed up in droves very, very fast.

But it is very expert in language processing, right? It got trained on text information, and it is fluent and it is confident. It's prone to inaccuracies. I'll go into it a little bit more. It simulates. It is not truly conceptual understanding. It's basically learned from all the text that it could get trained on, which represents a lot of, I mean, humans are very prolific in explaining and writing down text information, like a university library across the world.

That's where it's basing its knowledge on, and so it produces really fluent language. It is a very good language processor. So conversational tools that answer questions, summarize, explain and draft, that is an early entry point for generative AI. But of course there's also other content generation, and you've seen all these things online in the news. It can create images. It can create videos. It can create slides, spreadsheets, documents. It'll create computer code. Not going to go into all of that, but all of these count as generative AI and has a lot of capability.

But I want to emphasize that while these are accessible, and I didn't land on an exact analogy, but we'll build one here during this slide here, we want to make sure the human is in the loop. Large language models are a tool, and I would treat it as a team member that enhances our capabilities. It should never, ever replace decision-making, human connection and judgment.

Dr. Allan Wu 00:25:45
Humans are always, always going to be needed. And the idea is not just to shrink your workforce, right? It is to design a better team. It is not necessarily for a smaller team. We can do more, hopefully, if we use it responsibly and remember the tool is a tool. It is a computer. It is not a person, and it doesn't have any responsibility or real knowledge.

It is really, really good at being a super librarian. It will organize and summarize large amounts of information. All the information in the Parkinson's Foundation websites, which is quite a bit, is now accessible to you through PAM. It's very good at broadening your searches, broadening your horizons, broadening your brainstorming, providing multiple perspectives and options. That is what it's good for. It's like the super Labrador retriever. It will retrieve anything you want, as long as you tell it to broaden the search. It will do that.

However, these are designed to be overly agreeable to a predictable fault. So this is like the super graduate student that is able to retrieve anything you want, but basically has no common sense, has no sense of responsibility and takes no responsibility for mistakes. It's overly agreeable. It will get anything you want. It has amazing capabilities as a super librarian, but it will have no real pushback on anything you ask. And so you have to guard against that.

And so this has also been prominent in the news, but this is the reason why we use the term hallucinations for generative AI, where it just makes up output. Bias is very hard to detect. It reflects gaps and assumptions baked into its training data. Certain genders, underrepresented topics or people, not just people, but topics, are a form of bias.

So, for example, if you have Parkinson's disease and you are asking questions about it, well, there is a good amount of information out there about Parkinson's disease. But if you have some rare genetic syndrome, and I'm sorry, I'm going to make this up. I don't know, ATP17 G2 genetic disease, and there's only five or 10 of them in the world, then there's not much training data there for that. If you ask it questions about that entity, it will give you just as much information as the Parkinson's is, because it made it up, because it is so agreeable. That is a bias that, you know, if you didn't know that it was that underrepresented, you may not be able to tell.

And then AI slop has come into the lexicon now. It's copious AI-generated content without any human effort, often with generic, limited meaning and quality. So my advocacy to you is: be aware of this. Please don't contribute to it.

Dr. Allan Wu 00:28:55
Last slide on the AI 101. The reality is between the hype and the disaster. I think that the tools are, I've shown you that they're available to you. You can use them. There is, on this YouGov survey on the bottom there in purple, 19% of people did not use tools at least weekly. In the top bar, 30% of citizens have a baseline positive, somewhat positive view of AI.

But of those who use tools at least weekly in some form, again, there's a chicken-and-egg issue here, but in general, I'd like to think positively on it that if you dip your toe in the water, if you're using this at least weekly, you start to understand that there are ways to use this that are positive. Hopefully, this talk will encourage that 19% to 30% of you who are thinking about using it will take advantage.

All right. Part two, we're going to shift into some real-world examples of AI-enabled tools for people with Parkinson's and their caregivers. We are going to talk about symptom tracking and monitoring. We're going to talk about wearables and passive data collection. We're going to talk about communication, education, research, clinical applications and then a quick wave at the future here.

Symptom tracking, one of the first things that comes to mind is, you know, when you interact as patients or caregivers with the clinical care team, that is a very small percentage of your overall life. You have symptoms that occur daily, and it would be wonderful if we could incorporate those symptoms, your tremor, your movements, your cognition, your thinking, your impact on function, what you're doing every day, into the care plan in some way, and get good, reliable medical advice on how treatments can best be evolved.

This has proven to be tricky, right, because there's not a standard way to do this. So AI has the promise of integrating that together and producing graphs, summaries and so on. But the platform really has not coalesced in any one single way. So what I'm going to do is just show you different examples, a list of examples of where this is being done. But I think that this has a ton of promise, and it already is here.

Dr. Allan Wu 00:31:34
Many of you have already been doing this. There are standard rating scales. There are patient diaries that make it a little easier to integrate, at the cost of, you know, it is hard to put the information in because you have to remember to do it. But these patient portals and clinics and health systems will send surveys out to some of you. Some clinics will do that. Some don't yet. There are other various third-party symptom trackers like the APDA symptom tracker.

And there's a bunch of FDA-approved devices now that, for the most part, are monitoring tremor. They're monitoring other things, and many of them will also collect your own symptoms. Now, once you symptom track, or you spend the time entering it, and of course people have been doing this for a long time even in Excel sheets or pieces of paper, right? People do that. It's putting this together in a form that the care team can use, that you can use, grab insights from and take action on. Those are hard, and that may be something where the large language models and the generative AI can help. And machine learning is good for finding patterns.

So let me go into a little more detail here. This is an example of one of the, and I'm not endorsing this one over any other one. I was looking for a good example of illustrating how symptom trackers work. This is a free app, StrivePD. Probably many of you are using it, and if you haven't, feel free to look at this or any of the other apps that I will mention. But it's an app that you can manually log your symptoms, program in med reminders. It might, I'm not even sure, but it might even integrate with, say, your smartphone's health app as well.

It will sit there and learn the patterns and start to even find ways to remind you to do certain things. Then it will integrate with your smartwatch. So Apple Watch is integrated into this product, and it will monitor your tremor or your dyskinesia, and it will detect that passively without you having to enter in data itself. But if you do enter data in, like your symptoms every day, your mood every day or your med reminders that you've taken your meds, that information is all in here together in this app, and machine learning will start to derive patterns and can create graphs, summaries over a week, over a month, what's on average, and those sorts of summaries can be helpful for your care team.

And then they have another tier, and I think you have to pay for it, where it advertises an AI companion or concierge-style experience with human support. So this is obviously a large language model, generative AI, that is added on top of machine learning insights, where it will summarize things in a more concise way that might be easier for you to understand, use and query for new insights. I love the fact they offer human support because, as we're going to find out later in the talk, these generative AI things, the deeper you go, the more challenging they are to make sure that they are being completely factual and helpful.

Dr. Allan Wu 00:34:47
This is an example taken from their website, which is symptom log data recorded from symptom logs, daily check-ins and notes. It says it's generated by AI, but it's literally, it looks like something that you could just give to your doctor instead of your doctor asking you: so, have you had balance problems? Have you fallen? Do you have off periods? Do you have dyskinesias? And getting responses, and they dutifully transcribe it into the note. This could just literally go in the note. As long as the clinician reviews it and it's short enough that a clinician can read it, it can be in the note.

And then we get into this era, which I favor, which is patients and physicians co-creating the clinic notes. But that's a different topic. This is an illustration of how AI can be helpful. Again, not meant for treatment. That's where the word wellness is from. This is not vetted, and so you use it for advice, but it is not medical advice.

Okay, wearables and passive data collection. So I learned a lot in researching for this talk. There is a lot, and there's more than just what's here on this slide. These things all monitor different parts of potential symptoms that people with Parkinson's would be affected by: gait and tremor and cognition and sleep and speech. I guess the smart clothing and shoes are kind of on the gait spectrum, with smart socks and so on. Some of these will do some treatment or symptom management things, like the vibration for your gait or neuromodulation in the case of the Cala device there.

The asterisks are for devices that are FDA approved. But the FDA approval is usually very limited. It's for, you know, detection of tremor is the most common one, even though it collects data that can be used for other purposes.

So, all of these things, the message here is these are all machine learning based. Machine learning is the workhorse because it is capable of discerning patterns out of large amounts of data, and it's still enough on the left side of the spectrum where it has somewhat of a predictable output.

Dr. Allan Wu 00:37:15
If somebody is wearing a smartwatch and it detects AFib, and if you have the same EKG pattern, then AFib is reliably output. That reliability does not exist for large language models and generative AI, which is why generative AI is not what these devices can market as at this moment. None of them really do, but they all say AI-powered. You just have to understand, these are machine learning, which is not bad. Machine learning is incredibly powerful, but it is learning patterns, and it's not the same thing as what you're doing with the generative AIs in creating images and documents.

Now, on the other hand, for communication education, that is where large language models start to come into play, because we can do things like, can you explain the doctor's note to me? Can you explain the symptoms, diagnoses, test results and medications? Again, it does not replace medical expertise and context, because it will explain these things in a very generic way, and it may or may not apply exactly to your situation, though I will show you some ways to mitigate that bias and try to make these things a little more useful to you and your care teams.

I think interpreting medical bills and EOBs, whatever that is, right, explanation of benefits, can be helpful. Again, be careful what you put in. We'll talk about that later. Be careful what you put into these products. They're all learning from your data and information. Drafting letters, questions and summaries for you, your care team or your billing team, if your medical bill looks a little off, who knew. Pre-visit, prioritize your agenda with questions, translate your notes after the visit and create an action plan. I'm going to give you more on large language models in a bit.

There are also patient portals with chatbots, smart speaker assistants and website chatbots. These things are evolving, and they are helpful, and you can use them. You just want to be alert, even if it is provided by a clinic or a medical center that is very famous, it will still make mistakes, and you want to be on your guard for that.

Clinical applications: for this group, I want to just note that AI is starting to be used in clinics for clinical purposes, and you might come across these in your experiences. Ambient AI is probably the one that has the most penetrance into clinical settings because it has been shown to increase face-to-face time, save physicians time in writing notes. You should know that, and you should feel free to ask any clinics that do this, where is my data going? How is it handled? And in most cases, there will be a reasonable explanation of that. Some will de-identify, say, a part of the data collected for future training, and you should realize that you can opt out of these things if you wish.

Dr. Allan Wu 00:40:32
You might be sending patient messages through a portal, and the clinic may be using AI to triage those appropriately. People will write messages for all sorts of things because that is the nature of the portal. I need to change my appointment. I need this pre-authorization. My billing is wrong. I need to ask the doctor this clinical question. I'm in the emergency room, and I have these questions. So the urgency varies, the purpose varies, and the type of person who can respond to you effectively and efficiently is going to be different.

And we have seen clinics where the staff are not empowered to do anything, and everything goes to the clinician, burning out your clinician. Others are, trying to get through the staff is like getting through Fort Knox to get to the doctor's response. So trying to use AI to help triage that is an active area of development. Drafting initial replies sometimes occurs as well. That's gotten some more mixed response. But for the most part, patients will find that, gee, doctor, your responses seem unusually polite and professional. Are you okay? Well, it's because AI actually drafted in its wonderful fluency. They're still responsible for their content fully.

AI-assisted letters and notes. The next one, AI-assisted review of patient-reported data and remote monitoring data, is the machine learning and the large language model interpretation of the devices that I talked about earlier. That's an evolving space, but very active because of so many devices that are sort of in research. And I should say a lot of those things I put on that slide earlier are research and not actually generally available. I wanted to illustrate that this is an evolving space of monitoring symptoms.

And then an amazing opportunity is AI-assisted clinical trials matching. It really has not matured yet. NIH TrialGPT is a really interesting research project at NIH that showed a demonstration project for this. But, you know, Parkinson's Foundation, Michael J. Fox Foundation have, you know, Fox Trial Finder, trial matching services. They're not AI-assisted at this point. This is still an evolving opportunity.

Then, I'm not a researcher, so I'm not going to talk a lot about this, but suffice it to say there is a lot of AI working to assist in interpretation of these big data sets, including the PD GENEration, lots of opportunity there to identify patterns in that data set. So we appreciate everyone, patients, caregivers, controls, who are participating in these studies.

Dr. Allan Wu 00:43:35
Then future, I mentioned it once before, and I'll mention it once more here. Agentic AI is coming down the pike, really has taken off in 2026, particularly for writing computer code, but these are AI agents that act in the real world.

Some examples, and this has not really taken off yet as relevant directly to people with Parkinson's, but these are the ideas: it's continuous passive monitoring through voice, gait and typing patterns and acting on it and alerting you for things. So, for example, proactive outreach when the wearable data signals a change; adaptive medication reminders based on your on-off patterns, and depending on whether you missed a dose or not, or you woke up late, those sorts of things happen. You stayed out late. What should I do? Symptom triage, helping you decide what needs a call, what can wait, and then all the stuff I talked about earlier in the example where AI can reach out and help you schedule automatically, help you follow up on referrals to make sure they don't fall behind and that they get followed up with.

And this all leads to sort of our holy grail of precision medicine, where it's not just about genetics. It's about making sure that your treatment is tailored to you. If we do have the genetic information, which would be incredible, because everyone has a different type of Parkinson's most likely, that we tailor that treatment. We want to personalize patterns for your medication and deep brain stimulation programming, for those who have heard about that. It's a bit of trial and error, and wouldn't it be great if you could just make sure all the DBS programming is kept in mind as the next programming is done?

Especially accounting for the fact that Parkinson's, unfortunately, still is progressing. What worked before may not work as well now. How do you optimize that? Personalized exercise coaching and the learning healthcare system, where your data improves care for everyone, and your data can also improve care for you, feeding information back into the point of care.

Anyway, this is a lot of future stuff. I want to turn ourselves back to sort of practical reality now. Oh, yep.

Dr. Allan Wu 00:46:04
So, now we're on practical tips. That was sort of an overview of things that are in existence now at kind of a high level. I want to address the current generative AI and the chatbots, right? And so we would love your feedback on this kind of thing. But large language models are accessible entry points to this world of AI. There's a lot of free opportunity to use this. You can hardly avoid it if you use Google search, but you have to understand that prompting and interacting with these things are not search engines. You can, and people do, use them as search engines, but you get pretty generic responses.

And my point has been that we want to leverage this tool to be a useful team partner during your Parkinson's care journey. So, I keep using different, shifting metaphors because I didn't settle on one, but maybe you're getting the idea: very smart, capable partner with vast knowledge, overly eager to please and no sense of responsibility or common sense. So how do you corral this so that you're not led down a biased path and you're getting information that is reasonably trustworthy?

I have six tips and tricks. Again, those of you who are experienced at these may have come across all these already and more. But these are six that I chose that I thought could be helpful for a broad audience. The first one is, instead of using it as a chat, as a search engine, you want to use a standard prompt structure. I do try to think through this each time. Sometimes I get lazy and I don't, but it is helpful to remember it knows nothing, has no context.

So you have to tell it who to be, tell it what you want done, give it some context, give it the background it needs, and then tell it how to deliver the answer. You might say, help me prepare for my doctor appointment, maybe a few more than that. And then you can apply it this way and say you are telling the AI, you are a knowledgeable Parkinson's patient advocate. That immediately constrains the training set into the things that Parkinson's patient advocates kind of would say. Then help me prepare the three most important questions for my neurology appointment Tuesday. So that's what you want.

Then you give it some context, and there's some sort of medical generic context here. I even give it a website for the Parkinson's Foundation guide to preparing for a medical appointment, and I tell it how I want the information to be given back to me. This already helps constrain and give you more specific information right off the bat. It's sort of a good fundamental start.

Dr. Allan Wu 00:48:57
Now, I did say provide a context, but sometimes you don't know what the context is, especially if you are not an expert. For example, if I was using AI to help me plan a house remodel, well, I don't know the first thing about house remodeling details, right? I will have a general contractor come in and I will talk to them. But in the meantime, you can just say things like, you know, I want to plan a remodel and this is the kind of things I want to do. But before you answer, ask me any questions you need to help give a useful and comprehensive answer. That allows the AI to get the context it needs to be very specific.

So, I need to talk to my neurologist about whether it's time to consider DBS. You could ask that. However, if you just throw in this phrase, before you help me, before you respond, ask me questions you need to know to give me useful guidance, it might say things like, how long have you had PD? What medications are you on? How well are they working? What are your biggest symptoms? What are your concerns? What does your care partner think? Maybe I would have asked that question with context, and I would have thought of maybe the first two or three of those questions, but maybe I wouldn't have thought about giving number four and five there as context. But once you give it those things, they should give you a more specific answer.

Guide the output exactly if you can. This is where you can shape the output the way you want it to be. The basic idea is things like, give me a bulleted list, give it in lay language, respond at a sixth-grade level, respond in Spanish. Be very concise. I love the "be very concise." These things have a tendency to go on and on way more than I want. And so I have found "be very concise" is always helpful. But then you can go to the next level, and you can give it an example.

I will use Word templates or Excel templates and say, I want your response to fill in the blank cells in this Excel template. That worked well for a house remodel. A Word template, or you can just type it in if you want, but a Word template is like, I want a business plan. Here is the business plan format I want.

And you can also tell it how not to respond. That is also very helpful. For example, in full disclosure, I don't use AI for these slides. I will use AI to give a slide to AI and say, please give me a strategic opinion about what I put here. But these things have a tendency to want to just create a slide for me. I'm like, stop doing that, right? You know, respond with a text description and I will create all my own slides. Then you can coach it for better response. So you can go through this a couple times. You can tell the AI, review your response, how would you improve it?

Dr. Allan Wu 00:52:02
And it will sometimes give you a little more information. So this is a case where I said, hey, you help me prepare for the next visit, and I basically, please give it to me based on the Parkinson's Foundation guide for preparing for a medical appointment. In most of these tools, you can give it a website and it will read the website, and it will help you follow that output.

Okay. This is super powerful, and this is very helpful in sort of helping discern and protect against bias, right? I would routinely strive to ask for broad perspectives. I don't think it's really helpful, necessarily, to just have a conversation with the AI chatbots because, just like the overeager graduate student with no context, they will respond with one answer, which is whatever they think is most prominent in the training dataset. Then you respond to that, and they respond with one answer back, and you have no idea if they're leading you down the wrong path or not.

So, ask for more than one response. Ask for responses from more than one perspective. You can use this to have it role play for other things as well. Examples: respond as a professional physician, professional coach, professional strategy business administrator. Respond as a strategic consultant with deep knowledge about Parkinson's disease advocacy. The things I do a lot of is I say, give me pros, cons and alternatives.

I will sometimes say, ask for five different responses and options, and it will give me five responses, and that gives me an idea of the range of things that I'm dealing with. That's why I say it's very good for brainstorming. So you can say, first level, respond as my movement disorder specialist. What would concern you the most? And so on. You can simulate that.

This is super helpful for difficult conversations as well. I give an example down here on the bottom right. You can just say, I was asked to discuss the possibility of a feeding tube. I am not sure how to begin. If I bring this topic up to others, respond in turn with the perspectives and questions my neurologist, social worker, psychologist, pastor, family member and my patient would have. It will give you lots of perspectives, and it gives you an idea of what you're dealing with before you sort of zoom in on being more specific.

Dr. Allan Wu 00:54:37
And this is also a really, really helpful trick to keep in your tool bag, and you get caught by this. This is called show your work or show me step by step. This is an example that happened to me earlier this month. I did an analysis of some data in a spreadsheet, and I took a picture of the spreadsheet after I did my analysis. I just wanted to know if I was missing anything or if anything else the AI would pick out of the data set. So I said, tell me what you can conclude from these numbers. It said, that seemed a little more optimistic, didn't seem right.

So I went ahead and said, "Hey, I was thinking that it was a different answer. My interpretation was different. Please re-review your work." And it came back and said, "Oh, I'm sorry," and it gave me another conclusion that was aligned with what my interpretation was. But you could tell it wasn't quite right. You know how you can tell when the student is not quite right? They're like, "Please give us a book review of To Kill a Mockingbird," and they say, "There was a mockingbird and it was being hunted." You know, you didn't really read it, did you? Right. So I said, "Walk through your thinking step by step. Tell me the first set of numbers you worked with."

And then it said, I can't really read your spreadsheet. It came through blurry. It made up the whole thing, confidently, apologetically and wrongly. This is called chain-of-thought walk-through, thing step by step with actual details. Do this if you're doing anything that is going to be concerned about accuracy.

Memory, I think this is my last point. Memory, when we have conversations, is very important, and it's limited to each conversation you're having with the chatbot. So you want to save your work. You have to make sure it doesn't remember context. Every new chat you have, it remembers random things actually, but you want it focused on the context that you care about. So create a permanent context for each topic. These are called projects in Claude, custom GPTs in ChatGPT, agents in Copilot, terrible word. But I have a context for how to set up my internet system at home. I have a context for my financial planning. I have a context for my home remodel, right? So I don't have to repeat myself each time, and I can start a new chat and ask a different question.

Dr. Allan Wu 00:57:06
I don't have to keep repeating the context. And then be careful: if you're having a long conversation, like I rarely have a conversation with the chatbot that is longer than 15 minutes, 30 minutes at the most. Long conversations get distracted, lose focus, and inconsistently use parts of the previous conversation. It can be subtle and you may not even tell, but it starts to obsess over certain things. You can just stop the conversation and ask it to create a prompt.

I will usually read the prompt and change it a bit, but it's not bad. And you create a prompt and you can use it to start a brand-new conversation. It's a good trick.

I wanted to mention NotebookLM. It's a personal file cabinet. I have nothing to do with Google, unless I use Google. I actually don't use this tool a lot, but it promises that it only answers from things that you've given it. So it tends to have a lower hallucination risk and it can tell you if it does not find something. You can upload things like your visit summaries and test results, and then you can interact with it and ask it questions about your own personal items. I know we're running along, so I have basically two more slides to finish up here.

This is a potentially useful tool to look into, but I'm not endorsing it myself. I think it has promise. Two slides on responsible AI use. One is privacy in your data. Free tools are going to use your conversations for model training, and anonymous health data can still be re-identified. So please be careful. Don't put in things that are sensitive. Consider starting out, dipping your toe in the water, by paraphrasing things instead of uploading your whole clinical notes.

I think it's okay to take screenshots of parts of your clinical notes that hide identifying information. That's fine. You'll get used to what you can and can't do, but just be aware of what's there. When AI is used in medical settings, you can ask about opting out of the tool and what happens to your data. HIPAA still governs your health information. Anyone that is using AI for your healthcare is responsible for safeguarding that information. And you are responsible for safeguarding your information by all those things that you give permissions to on your website and on your phone as well.

Lastly, I mentioned the bias. AI tools can underperform, so please use some of the tips and tricks maybe to mitigate that. Legal accountability, it's still an evolving landscape. If someone has a harm from a diagnostic error, who is responsible? Right now, it's mostly the physician. So the physicians are responsible for what comes out of these AI tools if they're using it for advice. FDA oversight is very limited. It's only for medical devices, and almost everything I've talked about is not under FDA oversight because it's for non-diagnostic purposes, or they're consumer health wellness things, and they're largely unregulated. So, stay informed.

AI is a tool. It's evolving fast, and there are opportunities and some risks. Use it thoughtfully. Don't create AI slop. Know the limitations. Keep yourself in the loop. Start small and be curious. Ask questions of your care team. Contribute to the Parkinson community on how to use these things. I'm sorry I ran a little long, but I wanted to leave time for questions, so that's what I had prepared for you. I threw in some websites for things that you can use to get started. The Coursera course gets referenced a lot. I verified the hyperlinks, but not the individual courses. I think that's it.

Okay. Thank you.

Dr. James Beck 01:01:13
Thanks, Dr. Wu. That was really quite a tour de force. I mean, lots of information to cover as part of this process, for sure.

I just want to say that before we start taking the questions, just know that our team is organizing them as they come in and that the topics that we really have time for are the ones that are going to be, you know, that Dr. Wu addressed in his presentation. Nevertheless, we'll try to get to as many as we can, and know that my colleagues here are trying to do that. We also have, as will be mentioned later on as we close, our Helpline, which is a fantastic resource as well as part of that process.

Alan, it looks like you use AI a lot, but seems like there's a lot of effort to do that. They hallucinate, they get obsessed on small things, they can lose focus. I didn't realize that. I mean, I thought this was a computer and that was the advantage of computers, that they don't have these issues. So as you said, they show great promise, but they have these great pitfalls. How are you using them in your day-to-day life? You just mentioned you don't use them to prepare slides, but you use them to kind of make certain your slide makes sense, that type of thing. Is it something that you see professionals use more often? What about patients coming in as part of the process? Do they come in with what ChatGPT does?

I took your example and put it into ChatGPT for your knowledgeable Parkinson's patient advocate, to ask these three questions, and it came back with some ones that looked pretty decent as part of the process. So I'm just curious. There's a lot to unpack there, so let me give you a chance to respond.

And you're on mute.

Dr. Allan Wu 01:03:04
Yeah. I do use it every day. The way I use it, first of all, I do not think AI saves me any time. Now, I think that's very interesting because I have talked to a lot of fellow physicians that are using ambient AI, and it saves them tons of time. I actually use ambient AI as well in the clinic, and it does not save me time, because I actually review it very carefully. I'm a neurologist, and some of us neurologists are OCD and we want it to say what we really mean. But what it does save me is cognitive effort, right?

It is far, far easier to edit something that you care about than to write it from scratch. I think ambient AI doesn't save me time, but it saves me cognitive effort and leaves my brain alive to do other things. The other way I use, and I mostly use the large language models. I was on ChatGPT, and now I'm on Claude.

I interact with it, but I use these tricks that I showed you. I care about them because I shared them with you, and I really use all of them. I really do use context. I really use it to brainstorm. I really use it a lot to say, give me different perspectives, give me 10 different options, what are we missing? If I'm writing an email to, say, my chair that is kind of sensitive about various things, I will use AI to say, give me five different ways I would say this.

And then I will review those because it has to be my voice. And I will tell you, even though I use it and it remembers me, and you can give an example and say, this is an example of my email, use this voice, it does not do it perfectly. So I always end up writing my own anyway, but I will feed it back and say, here's what I wrote. What do you think? Give me three pros, cons, and alternatives, because I'm guarding against going down a rabbit hole.

So it's good for that, and that's how I use it. I don't use it for images and things like that, and they're never satisfactory to me either.

Dr. James Beck 01:05:25
Yeah. It seems like, as you described here in your presentation, it's really about how, if you're using these large language models, it's really about how you ask the question and making certain you're very specific about how you want that response and what they need to think about. You just can't go in and say, tell me about Parkinson's disease. You need to say, I have Parkinson's disease and I know something about it, so you can get to a different level as part of the process. It sounds like.

Dr. Allan Wu 01:05:57
Yeah, no, that's exactly right. I mean, with the modern tools, you can do things like, I have Parkinson's disease, I want to promote it, I want to be an advocate, create for me a website about Parkinson's disease, and it will do that. It will spend lots of energy doing that, but you weren't specific enough. It just spits out a generic website, right? It will answer like the no-context, no-responsibility thing that it is.

Dr. James Beck 01:06:25
Yeah, that's a good point. Hallucination is this thing to be on the watch for because it makes up answers. It seems to me that you're talking about it wants to please you and provide a response. It never seems to say, I don't know, which would be great. I would trust people who say, I don't know, because there's questions people just don't know. So how are you able, again from a perspective of a person living with Parkinson's who's trying to really get information, how do they know when it's hallucinating? How do they know when it's starting to make up answers when you don't have necessarily the context to come in with and say, oh, that seems bogus?

Dr. Allan Wu 01:07:14
Yeah, yeah. One trick I didn't say is you should always verify the source independently. It's sort of like getting your spam call and you happen to answer it because you were expecting a package from Amazon, and they got lucky and say they're trying to sell you something. So the advice is hang up, right? And then verify by logging in independently to Amazon or your bank and verifying it yourself. So what you do is you use these tricks I told you, which is walk me through this step by step, give me a range of options, give me the sources.

And then you don't click on the links in the chatbot, just like you would never do that in an unsolicited text message. You would independently open a browser. This is what I do, and then you go to that website and you read the website. Maybe I'm cautious, but then you go to that website and you read the website.

When I said at the end, Coursera course, I verified that link. It gave me a bunch of links because I said I don't take lay people AI courses. I wanted to give you guys something. So I asked it to brainstorm broadly. I asked it for like 20 different options. I picked out ones, and then I independently verified them. I will tell you what I independently verified was not what it gave me. I modified it and I changed it, so on the slide I was comfortable saying it because I'm responsible. I'm a human. I'm responsible for what I put on the slide. That thing is not. So verify independently would be the other thing.

Dr. James Beck 01:08:50
Yeah, for sure. Okay, some more questions that have come in as part of the process. A lot of people again are thinking about the availability of these tools out there, Copilot, ChatGPT, Claude. How do we know which one's more accurate than one? I mean, is it just catch as catch can, or are there ones that are really like, ah, this is the one to go for versus others?

Dr. Allan Wu 01:09:15
Yeah, I don't think I'm the best person to answer that. I think these are all evolving tools with varying levels of accuracy. None of them are perfect, for sure. Claude had some marketing things that occurred that increased its reputation. I think there are, and I don't know what the resources are, benchmarking tools for accuracy that are out there, benchmarking these things. I did not look at them because I equally trust and do not trust these things.

There is one called OpenEvidence that markets itself as being a little more accurate for physicians that want to research literature, and that's the reputation it has, but I can't verify that. And then if there's benchmarking tools, are they verified for, say, people with Parkinson's in the Parkinson's domain? That we don't know. One thing we could do as a community is say, here's the benchmarking toolkit examples that we could use to vet whether these tools work or not, right?

Then depending on the answers they give, followed by a group of experts, we could say this one works better for Parkinson's domain questions. But we don't have that right now.

Dr. James Beck 01:10:41
Yeah, for sure. I don't mean to put in a plug for our own AI chatbot, but I'm going to, and that's our Ask PAM. I think one of the goals here, and just put it in context if you will, I don't know if you've had an opportunity to see our chatbot, is that we worked very hard to try to minimize a lot of these concerns as part of the process. We've used what's often termed the walled garden. We give the AI language model free rein to go wherever we have already created materials, our website, all our stuff. It can't go beyond. This way, we've ensured it's a safe area, all the material has been vetted.

Often, if there's anything that's involved some kind of consideration about research or medical, someone has outside-reviewed it so we can ensure the quality of that. How does that compare to some of these other tools, which may see a broader realm? There's no walled garden, they get free roam of everywhere. What are the implications of that versus our PAM tool?

Dr. Allan Wu 01:11:51
Yeah. Well, I think the Parkinson's Foundation is not alone in creating these walled-garden things, right? There are a lot of specific AI applications of chatbots that are doing exactly that for different entities, different organizations. The American Academy of Neurology, for example, released a walled-garden version for us to navigate a very large annual meeting recently, and I can say that it did not work great. It had some limitations. That's why I mentioned NotebookLM at the end there, because that is what it promises, that it will wall it off for you.

You put information in NotebookLM and, yes, Google is probably going to learn from it, but it promises not to go outside the walled garden of your stuff. So if you put in, I don't know, all of your car maintenance visits, if you go to different car maintenance, and then you can query, when did I change my spark plugs? It will tell you, right? You can do the same thing for health.

But yeah, I think the other thing to look out for on PAM and these specific AIs is if you have an excessively long conversation, will it stay on track? That I don't know. I think most of these walled-garden things are discrete enough of a set of data that it can't get too distracted, but we don't know.

Dr. James Beck 01:13:19
So thinking about that, you had mentioned this idea of context, like a question that came in asking if contexts are something that you have to, like, are those part of the free version, or is that part of the paid version for some of these AI models? Do you happen to know? I imagine you're a big user, so you might pay for it, but I don't know if you know that.

Dr. Allan Wu 01:13:40
Yeah, I pay for one service at a time. But I think the free versions have these things because they do want them useful. If you use ChatGPT, look for , I forget what I called it. I had it on a slide earlier. Yes. And Claude has something called Projects. But yeah, context is, yeah, without context, it will give you generic stuff.

Dr. James Beck 01:14:09
Right, but my question, sorry, I maybe didn't ask it well enough, is if you have these contexts, is that not the same thing as a long conversation about the same topic?

Dr. Allan Wu 01:14:20
Oh, yeah. When I say long conversation, that means back and forth, say 50 times. You're having an active conversation with the chatbot. A context is where you can upload one large prompt and say, that's the context. You are an expert in the Parkinson's domain. You have access to everything in the Parkinson's Foundation, and you will answer in this. My goal is to construct a research grant to the Parkinson's Foundation for this purpose.

Then every time you start a new chat in that project, it knows that context as a prompt. Then you can have a conversation. Now, if your conversation goes 50, 100 levels deep and I've given it 20 different versions of a paragraph to react to, it's going to start getting confused. So I should start over and say, okay, I'm back at working on the method section of this paper. Here's, can you react to this? It will forget about the first version I did, but it will still have the context. You can have as many chats as you want within a context. You just don't have to type it all again.

Dr. James Beck 01:15:36
Got it. Yeah, I appreciate that. For people with Parkinson's, the less typing the better as part of the process. I couldn't imagine.

Dr. Allan Wu 01:15:46
They also take voice now. So most of the versions now will allow you, if you click the microphone, to just talk to it and it will transcribe the words using natural language processing. Then you can also set it so when it responds, it'll speak back to you.

Dr. James Beck 01:16:02
That's fantastic. That's going to be really helpful.

Dr. Allan Wu 01:16:05
Yeah, that actually is available in most of the models that I am aware of.

Dr. James Beck 01:16:09
All right. Well, at that point, I have to think we have to close. Thank you very much, Dr. Wu, for your time. It's been fantastic. Really appreciate that. A lot of information to share today. I'm sure we'll work on trying to get this turned into a fact sheet. Maybe we'll get AI to generate one for us from here. We'll probably actually get a real person and you as part of the process, but this is really useful information for our community. So thank you very much as part of that.

I know we had a lot of questions come in during our Q&A. Unfortunately, we could not get to them all. If your question wasn't answered, feel free to call our Helpline at 1-800-4PD-INFO, or you can email us at Helpline@Parkinson.org.

This finishes our spring series of expert briefings for the year. We'll come back again after a brief hiatus this summer and come back about disease modification and stem cells: where are we now?

There's lots of exciting information you've probably heard about in trials that are going on. Hopefully we'll be able to give you some feedback on an update of where the state of the field is as part of the process. Then, I think everyone's favorite coming up is the gut-brain connection in October. Then we'll close the year with environment and Parkinson's. This is an area that the Foundation is getting into, the idea of what could be environmental triggers for Parkinson's and how that might actually lead to ways to prevent Parkinson's for future generations, which would be really fantastic. You can always learn more and register at Parkinson.org/ExpertBriefings.

Our PD Health @ Home series is ongoing, offering a range of virtual programs, Mindfulness Mondays, Fitness Fridays, and you can learn more and register for PD Health @ Home by visiting the webpage Parkinson.org/PDHealth. Last, I would like to thank the Light of Day Foundation, who has always been a tremendous supporter of the Parkinson's Foundation and whose generosity here has made this programming possible. So thank you, Light of Day Foundation.

We're here for you. Again, you can reach out through our website, our Helpline and our toll-free number as part of the process. Until we meet again this fall, hope everyone has a good summer. Take care, and we'll see you on the next Expert Briefing.