ep 32: AI & LGBTQ Healthcare

Speaker A: Hey there. I'm Gilad Barash, and welcome to who's yous Data? The podcast that deals with how data influences life and how life influences data, the human side of data analytics. Hey everyone, welcome to another episode of the who's yous Data Podcast. To me, one of the most interesting and rewarding use cases of AI is providing access of life changing services to minority minority populations who are underserved. Nowhere is this more true than in the medical field and specifically in the quote unquote niche field of LGBTQ health. Today's guest is Dr. Roeet Sukeh, Director of LGBTQ Health Services at Clalit, Israel's biggest HMO and Chairman of the Israeli LGBTQ Medical Association. We chat about the state of LGBTQ medicine in Israel, difficulties for its patients, and trying to seek access to services, and what his vision is for using AI to improve these services. We chat about how doctors need to adapt to the AI age and the fact that AI chatbots can provide safe spaces and be more compassionate or with less judgment to LGBTQ patients and give them services. This being who's yous Data, of course, we talk about privacy and bias in the data. We also discuss one of the most interesting healthcare use cases I've encountered, using AI for harm reduction and drug use, and how that relates to getting past the safeguards of public generative AI. Lots of fascinating topics in this episode. So let's get to the interview. Okay. So, Dr. Zukor, welcome to who's yous Data podcast. It's great to have you. Can you tell us a little bit about your background and the state of LGBTQ health in Israel?

Speaker B: Yes. First of all, thank you, Bilat, for having me here. Um, really honored and, uh, yeah, sure. So. So first of all, um, I'll tell a bit about myself. My name is Roy. I'm an internal medicine and infectious diseases physician by training. And in 2020, I've actually did the first LGBT medicine fellowship at the Mount Sinai in New York. This was the first time that this kind of a fellowship started there. Now it's already the fifth year that it's going on and it's. It was endorsed by the American Medical association and it's now happening in a few more countries. Uh, and when I came back to Israel, I decided to take this role to a more kind of a leadership role. Uh, which means that today I'm sitting in the main administrations of Clalit, which is the main HMO in Israel. Overseas, 5 million patients have more than half of the Israeli population. And on the other hand, I'm also directing the health services, the LGBT Health Services in Tel Aviv Medical center, which is the main hospital in Tel Aviv. In both of those roles, I'm actually, I have my clinic twice a week. Ah, still to keep things going on and feel a bit of a doctor also. Uh, but most of the time I'm just here to create new services to the LGBT community from the notion and the acknowledgment that the LGBT community needs, kind of, there are specific needs for this population that needs to be addressed situation wise in Israel today. So it's very complex because on the one hand, we know that these days it's, uh, not the best political, uh, era to the LGBT community also in Israel. Regarding the people who are part of the politics here, but taking politics outside of it, when we look at the, uh, situation in Israel, especially when we are part of the Middle east, for sure, being part of the LGBT community, if you need to choose one place in the Middle east, it would be Israel, because that's the only place you can be part of the LGBT community, uh, and no one will do anything about it. And on the other hand, legislation wise, I think, like, uh, you know, uh, during the years, more and more things towards and pro the LGBT community has started to, uh, appear. I think like one of the epics was two years ago. And also even surrogacy for gay couples, uh, is already authorized in Israel and it's part of the health basket in Israel, which is amazing. Uh, of course not paying for the surrogate, but, uh, all the other procedures needs to be done. But, you know, generally, except of gay marriage, when you look at the big things, which we still have a way to go, but we are in a quite a good situation, uh, relatively to the era we are existing in today, globally also.

Speaker A: Thank you. Now, full disclosure, as a, you know, a proud member of the LGBT community myself and, uh, being a member of the lgbt, uh, clinic in Tel Aviv, can you talk a little bit about. I find this always a little bit challenging when I talk to, you know, the straight people. Uh, what have been some of the traditional difficulties for LGBTQ community when it comes to accessing medical services? I know anecdotally, I can speak for myself. I know that there is, you know, you know, just going to any doctor and talking about situations in health and things that are specific to our community can be uncomfortable. But can you talk a little bit more just kind of to set the table? What are some of the, you know, traditional challenges in gay health, lesbian health, Trans health when, when going, trying to seek access, uh, to medical services in general.

Speaker B: So first of all, you did right by actually, you know, the LGBT community is like a, uh, being put it all together at the LGBTQ plus, but each one of those letters, um, inside of those letters, there are a lot of things regarding medicine and health that we need to take care of. And a lot of the times it's not just about putting everything in one basket. Generally, when we are talking about LGBT medicine and health, we're talking about two sides to this coin. The first one is that even in 2024, if you're not in, in the LGBT Tel Aviv clinic, a lot of the LGBT community are still afraid to go sometimes to healthcare providers or just, you know, people from the healthcare system, uh, and they are using much less of the health system because they are afraid of either discrimination or disclosing themselves or it seems to them sometimes, you know, when you are living, like when I was living in, for example, in near Haifa, and my family physician is the family physician of all of my family and, uh, no one knows about me. It's a big issue. And even sometimes it's not supposed to be just about, um, they're afraid of being discriminated. But having a bad experience with the systems before will make you not come again until the moment you really, really need to. Meaning, like, and sometimes it doesn't have to be like something really big that someone really said something terrible for me. I always remember, you know, the secretary in my clinic in Kiryat Motski near Haifa that was always asking me, so what about a girlfriend? How come you. And you just don't want to deal with it exactly. Like, you don't want to deal with like going to, uh, uh, weddings 20 years ago. And when everyone asks you, you know, what's going on with it, like, how come soon you will find someone, don't worry, a woman, of course, etc. So this is one, ah, one, on the one hand, people are using less of the health system when they are part of the LGBT community. And this is, um, you know, in, in studies that we see also, we see it all the time in the global way, not just in Israel, by the way. And on the other hand, we are talking about proper medicine. When we are talking about medicine, each one of those letters underneath it, there are a lot of medical issues that needs to be addressed. For example, as a gay man, there are specific vaccinations that you need to do much more than the other, uh, uh, people which are heterosexual uh, we can offer you things like pre exposure prophylaxis for hiv, meaning like a prevention therapy for hiv, which is specifically nowadays more towards the gay community. We can talk about things. There are sounds, um, gay couple that is coming to start the process of being a parent. Okay. Even in surrogacy or talking about, um, both edges of the sphere of being adolescents. Adolescents and being a geriatric patient in the, in the gay community. And each one of those are things that in a perfect world, when we don't need to talk just about how you give safe space to the LGBT community when they are coming to use health systems, we can talk about what is next, how you create a cultural based or uh, um, you know, adapting the system to different cultures, to different kind of people, um, and knowing what to suggest to them because of their, in this case, their sexual orientation or their gender identity. And by that doing a better medicine and having a better system that notes to provide you a better care also in the prevention.

Speaker A: Yeah. And I think, you know, even from my own personal experience going in there, there seems to, there's a lack of, uh, I guess I would call it cultural competency when you go to doctors who are outside of the community and don't specialize in it. Ah, then like you said, they really don't know the special needs and even the medicines. I remember and you know, I was in South Carolina and had to go to emergency medicine and they had no idea what prep was, what Truvada was. And so that does not, um, and for those of you listening, that is what he was talking about was the pre exposure prophylaxis that is very common in the gay community, um, taking a pill to prevent, um, HIV exposure. So when you go to doctors who are not aware of that, it sort of, it also will reduce your confidence in the care that you're getting. You had written an article that caught my attention about what you called medicine 3.0 and how AI can be utilized in LGBTQ medicine. Can you start by just kind of explaining what, what does that mean? What is the progression from medicine 1.0 to medicine 3.0 and how does AI fit into that framework?

Speaker B: Yeah, first of all, just for a disclaimer, all this area of like, uh, medicine 1, 2, 3.0 are taking basically from, uh, Peter Attia, which is really known for the era, the area of longevity is a very famous, uh, physician who took it to how do you make medicine better and give more focus to the air to prevention. And from that I elaborated a bit more and decided to take it to um, another areas in this. And we are talking about medicine 1.0. This is like the good old medicine where it was a reactive medicine, meaning that when something happens to someone, you just treat it. You don't like, uh, when symptoms appeared, you just treat it. And sometimes you, you treated it in, you know, um, quite a long time ago, 100 years ago, you treated it according to what you thought would be the best for the patient. Meaning like we had less of a guidelines, we had less of a evidence based medicine, but more of like the good doctor that, you know, he is good and you are following his intuition sometimes. And this is what happened. And it was a more more reactive. Meaning like you won't do a lot about prevention, but you would do more about doing something. When something is happening and you need a surgery, you will do it. You have, uh, you know, um, something like that. When we're Talking about medicine 2.0, this is the era in the last 50, 60 years that talks about evidence based medicine, where we have more data, we know things more in a global way, we know how to treat things, um, I wouldn't say personalized, because it's not really personalized, but more of you know that after checking, for example, thousands of people and treated them with something and not treating them with something, that this treatment was beneficial and this is how you should do it. And then when it comes to the guidelines, everyone, every doctor will follow those guidelines. And when something happens, this is what you need to do. And when we're talking about the next generation of medicine, it's really medicine 3.0, where you give much, much more attention to preventive therapy, to personalized medicine, to how you look at the person as a person and not like someone inside the big guidelines that were created, taking it more to the personalized level, which I think, by the way, that things like AI can really help in it. Because if you think about it, most of the reasons we cannot do this really good medicine that uh, talks about really, really personalized medicine is that it's very time consuming. It requires a lot of manpower these days, at least it requires a lot of physician and medical staff and that things might change, ah, I think during the years, uh, and it's going to change really, really quickly. Also in the, you know, AI era that we are at today, when I'm Talking about medicine 3.0 at my level, uh, regarding LGBT medicine is how do you give attention to who you are regarding, like, if you are part of the LGBT community, how things are Tailored, made for you. And if you, if you have, you know, a gender identity issues, how do you give the best medicine to those people who are, um, either have, uh, gender identity, uh, disorders or not disorders, by the way, or having, um, different sexual orientation. And to suggest to them also in the prevention medicine, um, but I'm not just talking about LGBT medicine because if you think about it, it's really true for even things like gender medicine. 51% of the world population is women. When you look at studies that were being done for years and years, a lot of those guidelines that we just talked about regarding medicine 2.0 were being built on, um, men. A lot of those, um, you know, from dosage of drugs and a lot of those kind of things was all being decided according to what happens in a men body and not a women body. And then, uh, when we talk about that is how do you make medicine more oriented towards women versus men, for example, which is a huge issue.

Speaker A: You bring up an excellent point that, uh, traditionally medicine has been very male focused. And that is even evidenced by the fact that only in recent years, the whole area of what's known as femtech or like feminine technology for health or women's health is only now gaining traction. And there are more and more companies around, startups around everything from ovulation to menstrual cycle and fertility. Mental health. Yeah, hormone therapy. Right. And so one of the issues that I want us to talk about in a minute is the issue of bias in the data and the lack of, uh, data for LGBTQ community. But this is actually juxtaposed against the fact that data about women, like you said, 51% of the population, like, the issue wasn't that there wasn't data, the issue was that it wasn't utilized. And this is the thing that interests me about this intersection of social issues and real world with technology, is that you are finding a use case that is going to be so impactful for a vulnerable group of people that, that are underserved. That I think is fascinating.

Speaker B: It just reminds me that one of the things that I really, um, even as a very, uh, young doctor, one of the things that I really didn't like about medicine a lot of the times is that even today the medicine is still very paternalistic. Meaning like that, uh, a lot of patients are. They are those patients who really not likes it. Like, the doctor will tell me what to do and I will do it. But in an era when you have so much data out there and things are changing all the time, A lot of the physicians are not really updating, including myself. You know, there are areas that I'm not really updated and I'm going back to, um, you know, the, the websites, the, the guidelines, whatever. And I think that one of the things that all this era of AI and, and data generally is bringing is, makes physicians be less, they have to be less paternalistic because it's not an era that we can say, I'm the doctor, I'm right, this is what you should do. Because people can check, and sometimes they cannot even check and understand that what they are actually bringing to the table is sometimes even better than what the physician can suggest. And this is kind of sometimes starting to be a bargain between the physician, uh, that needs to take his ego a bit off and actually comes in an open mind to learn on the one hand. And on the other hand if you create this kind of a relationship, I think that the place of a physician is still very important even nowadays. Mostly because even when you have all the data and you know, what you need, sometimes presenting the patient all the things he can have and what are the cons and what are the pros of each one of them and let them decide on their own. Uh, at the end, I think this is how medicine should look like. So using the data and bringing the data, it's not a much of a problem. But making the um, the right decisions, this is where physicians needs to come back to the table.

Speaker A: And I think that one of the, the way that I look at it, I don't know if you've ever, if you read the story, it was in the news, think a year ago that some mother of a 13 year old boy that had some neurological problems went to uh, 13 different doctors, did a bunch of different tests, nobody diagnosed, everybody misdiagnosed it. And then when she fed all that information into Chat GPT, it came back with a diagnosis that when she went to the doctor again, turned out ChatGPT was right. I think that it's problematic in the way that it's presented because it's not right. You have to really think about what this means and what this doesn't mean. It doesn't mean Chat GPT is a better doctor than these 13 other doctors. They kept sending her to do more tests. At some point they had all this wealth of information, this breadth of information. And I think what ChatGPT did well was to synthesize it all from all these different sources. And so, you know, maybe it's that it becomes another data point that a doctor Considers because it does some kind of analytics on the data, that humans are not as good at being siloed and being more, you know, specific in their, in their, uh, areas of expertise.

Speaker B: I don't think even that the competition needs to be really done between, uh, AI. Right. It's not a competition, it's a mini. The meaning is, and, and they are all kind of, you know, that you give a ChatGPT or other platforms, um, to do like the medical exams that the students are doing and who is winning there. And sometimes the AI is even better. But the thing is that, you know, as a human beings and as those ones who actually created eventually not us, but created the area of AI eventually, like every good thing, it will be hard for others to, you know, adapt it at the beginning. But the main idea is how you use it to utilize your time in a better way. Even as a physician, when you are using your time so much for just like, ah, clicking all day and doing administrative stuff and doing all those kind of things. Um, and I think this can be. And there are quite a few companies today that are actually, um, doing those things like taking what you speak about either with patients or in the mental health. Um, you don't have to actually write what happened during the meeting, but everything is really coincide to a, uh, very nice summary, even sometimes with suggestions. So it's fine. I'm not sure that just like, uh, as a physician saying, no, no, AI is not for me, I don't believe in it. I think it's a huge problem because it's not going to change and just going to be bigger and bigger and we have to know how to work with it and actually utilize it in a way that will help us also.

Speaker A: I don't know if you've heard of this study, but it turned out that they created chatbots that the patients could, uh, interact with. And they actually preferred interacting with the chatbots than with the real doctors because they sounded more human and empathetic.

Speaker B: So actually it's amazing because this is. It takes me exactly to where I want to take you next. Okay, the big question. We spoke just a few minutes ago about LGBT community and how they sometimes afraid to go to physicians or how they are afraid of utilizing systems because they're afraid of being discriminated. They feel sometimes that physicians or, um, the medical staff might be LGBT phobic. And I'm asking myself, can a chatbot or copaibot or whatever you will call it can be more compassionate and more understanding than actually the physician you are Sitting. And he comes with a lot of, you know, not, not as a clean slave and some. And sometimes with his own opinions about who you are, what you are.

Speaker A: Yeah.

Speaker B: And you know, there are so many. Each physician, we are human beings at the end of the day, but what can we do? And we are coming with our opinions about things. Learning as AI machines and AI systems. How to create a more compassionate talk with the patient might really, really change for the LGBT community. Meaning like. And, uh, this is a project, actually a small project I did with Microsoft two months ago, is actually taking the data, um, from different data about LGBT medicine, taking it from all kind of sources. Ah, Using a healthcare directories for physicians who are LGBT friendly, by the way, and creating this co pilot, which, you know, when you're sitting in the middle of Arizona, I don't know where, and you, uh, feel alone in the world and then you can just ask something. The co pilot, he will be very, it will be very nice to you. It will create kind of like even sometimes interaction that will create even maybe some emotions. And, and later on you will also get the best answers from the best latest guidelines from this copilot. And when it's, you know, it's too big on him because you need some treatment, you need to actually go to a physician and do something. He will tell you, okay, dude, um, it was great. But I think you still need to see someone. And if you need to see someone, according to my knowledge, you should go to Dr. John Doe, which is just around the corner. And, and he's a, uh, very LGBT friendly kind of a doctor. You know, when you create this kind of a system, you know, there are those points that you still need to go to a physician, but there will be a lot of points that it can just end up there and you can get your answers because just right now, you know, you were with someone, uh, and something happened and you're not sure if you need to go to the er, you need to go to whatever you need a post exposure prophylaxis, all those kind of things. And then you get a reliable answer, which actually sometimes can be even better than, um, um, physical physician. Regarding the answer. And if you are okay with that, that's okay and everyone is happy. And if not, the system can always refer you to someone who will give you a safe space.

Speaker A: I think that's really, really important. And I love that, uh, scenario. I think we joked about it, but I think having an empathetic ear available to you where you can be honest about your situation would Be very, very important. You talked about this sort of cultural bias, perhaps, that a doctor might have and I, and, you know, we've touched upon this, but I want to dig a little bit deeper in that, because this is one thing that I always. That concerns me with when we're talking about using AI for, you know, certain causes like this, is that there's issue of bias in the data. LGBTQ community in general. The population is a small subset of the general population, and that makes it statistically challenging, probably a, to find a subset in order to do studies in order to build AI models that are robust. Do you have any thoughts on some strategies for mitigating issues like this, like sample size bias or just historical biases in the data, such as discriminatory policies or practices and how we could address this in using LGBTQ data for AI model training?

Speaker B: Yeah, so, first of all, we, uh, should remember that most of the studies that are repeating on and on are always talking about 8 to 11% of the population is part of the LGBT, uh, community, which means that, uh, this subset is not that small. It's like 1 to 10 supposed to be in this kind of like a rainbow of people who are part of this community, which means that, uh, they are there, a lot of them are not out, and we cannot find them. So always it seems that the subpopulation is very small, but it's not the case. And sometimes one of the biggest problems of systems is that because it's a very delicate situation and a very sensitive, uh, one, it's really hard to ask someone if he is part of the LGBT community. Even in studies or when you do some studies, how do you do it on the right way to actually get to those patients and according to them, to learn how to help others from the LGBT community? And that's the main problem is how do you reach those people in order to do those kind of studies? Now we can talk about a lot of ways to do that. I can tell you about amazing thing I did with, uh, Clalit. So when monkeypox was, uh, happening, uh, the outbreaks of. The outbreak of Monkeypox started in 2022, and there was a lake of vaccines in Israel. Also we had only like in Klalit, we had all over just like 5,000 vaccines. And we had to decide who will be the one who gets the vaccine and who will not. Now we have many more than these number of gay people who were the indication to get that. Uh, and how do you decide who should get it and who should not? First of all in our systems, it's not legit and we can talk about it if it's right or wrong, whatever, but it's not legit to put on someone in diagnosis of a man who have sex with men. Right. But now for some people, as I see it, like sitting in the middle of Tel Aviv, living my nice life, I'm saying, okay, if it will be written about me that I'm gay, I wouldn't care as long as I know that because of that the system will know what to offer me because I'm gay. And what happens today is that it's really hard to find those people because they don't have a diagnosis. So I cannot know how to say, oh, you need an HPV vaccine or you need a prep, or what about that? And what about the others? Or you're a lesbian woman, so I know that you have a higher percentage of uh, breast cancer. We can talk about it. Also, how do I approach as a system and telling you, don't forget doing your mammography, uh, because you didn't do it and it's important for you. Yeah. So we don't have it because we are not putting those signs of people in their systems, but we still can find them. And what I did with Claude is thinking who might be gay in the system. And then you take 5 million patients, a subset of a big data that uh, you don't have almost anywhere in the world like they did in Covid. And this is where I took my uh, you know, uh, inspiration from. And you say, okay, if he's doing swabs for rectal and pharyngeal, most probably, and he's a man, most probably is gay. If he's on prep, he's gay. If he did like a vaccine for human papillomavirus from the age of 27 to 45, this is the only indication only for men, so probably is part of the gay community. Right. So I started doing those kind of things. And we just went to the system and show and looked how many people are we talking about? And out of 5 million patients, suddenly we went to a subset of 8,888 people. That was the number.

Speaker A: Mhm.

Speaker B: Now in this time M70 people in Clalit got infected with monkeypox. 69 out of 70 were part of the 8888 cohort.

Speaker A: Wow.

Speaker B: And it shows you how you can still easily find people according to what they are, how they interact with the system, just to understand who they are, which is nice on the One hand for research. But think about it. If it goes to the wrong hands. Right, Right, absolutely. And this is always the, uh, kind of like a game between, like, keeping discretion and on the other hand, and not to prevent the right medical attention for those kind of patients. And I think, like, I Hope that in 10 years from now, I'm optimistic it wouldn't be so hard to put on someone, a gay, uh, men who have sex with men, because the system will know what to do about it. But as long as we are for now, I can understand why it's so complicated.

Speaker A: Yeah, absolutely. And, um, you know, it's similar in. In what we used to do in ad tech, where it really was sort of behavioral. You were looking at behaviors that indicated a certain proclivity or an interest in something or being part of a certain population, because you couldn't. There are privacy restrictions around demographics and making those. Those kinds of statements. That, that's really cool that you. That you managed to find behaviors that so accurately portrayed people that brought them into the population.

Speaker B: That, uh, shows you how can you do it. You know, it's even easier when you can actually look at some. Someone and where did he go, to which websites he went, or what did he do? And in a minute you can understand that. You know, today we all know that, you know, whatever you do, you can't really hide it, and it's really somewhere there. You know, the system. You want to enjoy the good things that the systems are bringing, but the price of it is losing totally your, you, uh, know, uh, discretion.

Speaker A: Yeah.

Speaker B: Your personal life, your privacy, everything. So. So it's a bargain. And I think, like, the systems makes us, uh, choose, actually, because. And most will choose to just let go.

Speaker A: Yes. It's a devil's bargain. And that's a good point. And, you know, that was another concern. Um, another thing that I always think about these things is that certainly in medical research in general, there's a huge concern around privacy, and so of the patients, and particularly when you're talking about lgbtq, as you had mentioned. So there's definitely considerations around that. I think that one of the issues with bias in the data, and like we said, that there's a less, uh, representation in the data, I think, for AI as well, moving forward, rather than trying to fix the data, if you've got underrepresentation of certain classes, the idea of saying, you know, stratifying it by these population traits and building different models for each one of them probably is a better way to go rather than trying to fix One model with some biases in it. So doing one, you know, just like if it's a model for women versus a model for men or for you know, gay, uh, health conditions versus lesbian women, health conditions, etc. Each one of those, like you said, um, right. And even with um, you know, breast cancer, maybe rather than trying to adjust for bias of sexual orientation in one model for female, um, breast cancer, it's creating models for lesbian breast cancer. And so you'd have hopefully a better signal to have a more accurate model and be able to have better outcomes.

Speaker B: Yeah, there are, by the way, um, Microsoft, when we did this small project, uh, one of the things he's talking about, they have their own features, new features of what we call safeguards in this thing. We also need to think about like where are, you know, when you are talking now? One of the things I'm doing a lot is talking about harm reduction to drugs. This is like my, one of my babies. And when you are talking about drugs, you know, if you go to um, you know, an AI, um models probably if you'll say okay, I'm doing now coke and um, I don't know, ketamine, is it okay? Yeah.

Speaker A: Yes.

Speaker B: And of course, you know, it's making sense that the system, without thinking so much will say I'm sorry, I'm not allowed to whatever, um, going through this thing. But on the other hand when we are talking about, we are talking harm reduction. So how do you, how do you make those AI models to answer questions that might save your life? Because you are going to do it. But a minute before you just wanted to see if it's fine to drink alcohol and using ghb. Now you are afraid of asking your, your physician about it, of course. And you are alone in the world, you have no idea what to do and someone is telling you that's okay. Now if you can ask the AI model is it okay and will tell you, dude, this is a very life threatening thing and you should believe me, avoid it. Instead of saying I'm sorry, I'm not allowed to answer it. This is a life saving matter. Right. On the other hand, those systems do not want to show the world that they are pro drugs or something. So it's again like uh, it's something that I'm doing. Not an AI model all the time is like, how do you create those disclaimers saying like uh, the system is saying, listen, drugs are really, really bad for you. Generally we don't know when you're taking something. We have no idea what you're taking. If someone is telling you this is G, it might be something else.

Speaker A: Yes.

Speaker B: And it might affect each person in a different way. Having said that, and this is illegal also, I'm just reminding you, but having said all those disclaimers, although I told you it's not a good thing to do it if you decided to do it anyway. So just know that this mixture is very, very bad for you. So how do you make those AI models creating those right. Disclaimers that will, that will answer like I just answered you right now instead of just wiping it out, you know?

Speaker A: Mhm. I think that is really fascinating and I think, you know, in general harm reduction has always been somewhat controversial. Even you know, if it's uh, handing uh, out needles to avoid HIV infections and uh, giving safe spaces to, to do drugs, etc. Has always been. I'm coming from having lived in California and New York, I remember there's been a lot of discussion around that. And the issue of uh, party drugs in the gay community certainly is one where you can do harm reduction. I think it's a really great idea. That is fascinating. I think that really, really requires a lot of thought but would really, really be helpful to have a little chat bot on your phone that can help you make the least bad choices.

Speaker B: Right now I have things that I did by the way, like um, I had a mixture table that is now on the version number three of it which is going all over the country and people are using it. And now I created a digital platform to it. But it's not AI, but having your um, this thing that will be an AI model which will be your accompany in every think of the way and you want to ask something about something also those kind of things. I think it might be a great idea.

Speaker A: Definitely agree with you. I think that that would be fascinating. By the way, just out of curiosity, you had mentioned, you know, the study that you did for monkeypox and I, I had this observation. One of the things, another topic that comes up when you talk about AI and, and how its proliferation in society is a question of uh, the acceptance and people being willing to use it. Just an observation. It seems to me historically the LGBT community has been quick to adopt technologies that benefit them, such as HIV medication or the monkeypox vaccine. I remember I was living in New York and the minute it came out, you know, everybody was out trying to get it. There seems to be a lot less, uh, you know, vaccine hesitancy for example, uh, in the community is that your Experience as well. And do you anticipate, what do you anticipate regarding their willingness to utilize AI driven healthcare tools?

Speaker B: So I think generally, um, and it's generally speaking because you know everything is like eventually we are all different persons. But as a population the LGBT community and ah, specifically the gay community is very adaptable to technologies changes. They are sometimes the one who leads it. Also I'm always saying like um, you know, if you want to know where to buy your apartment, just go and follow the gaze. Right. Bringing gentrification and it's the same in technologies and adapting technologies. I feel that a lot of times we are one step ahead by the way also with drugs because now you can see it everywhere.

Speaker A: Uh, that's true too. Yeah.

Speaker B: If we take it to the bedside or whatever, the gay community sometimes is a good mark to see what will happen next. And I think that it's also if you think about it it's quite easy. Although a lot of them will, you know, the discretion and everything is important but they are quite easy to target when we are talking about like you know, how to reach them meaning like with social media and using social media and using platforms, web platforms. So sometimes they are really easy to um, target and then sometimes you use this group to learn about others also. And this is happening a lot of times in marketing and a lot of other ways to use the LGBT community as a targeting uh, population to see what's going on there. And it's also good, bad, whatever. But if you also like it depends where you go because of course a specific kind of people will go and use all kind of like uh, very kind of groups and those kind of things in Facebook, Instagram, whatever those one will use it were usually the ones who don't care about showing themselves out. But when you're talking about like a set of data for example of a gay apps, ah, like a grindr for example, there you have a subset of people who are like in a black boxes, you have no idea who they are. And this is sometimes the only way to approach, coach them to do tests, to know what to do all to, to bring some um, news to them. And this is the only way they are going to uh, get those news and uh, and all this information.

Speaker A: And I will say that also the gays we know, we can always tell you what divas are up and coming. So always listen to us. And, and one more thing about that, it, I, I don't know if you noticed it but there was some tipping point when straight people started talking about poppers. Yeah, that was always our little secret. But then suddenly it's out and everybody knows that now. It's not.

Speaker B: Fortunately, with ghb, for example, it's still somehow, um, in the straight community, it's still about, ah, raping drugs.

Speaker A: Yes. It's taboo.

Speaker B: Very, very bad, uh, reputation if you are a straight person.

Speaker A: Yes. And it should stay that way.

Speaker B: Yeah. You can take the crystal map if you want.

Speaker A: Yes, please take that away. That is the worst. Yes. Um, I think maybe that even came in the other direction because that also in the, in the last few years has just blown up and it's, it's horrific. So, looking to the future, what are your goals for medicine 3.0 with the LGBT community? Um, where do you see AI or you trying to, um, integrate AI next? And are there any kind of specific technologies or research areas that you're particularly excited about?

Speaker B: The first of all, there is always like this slogan I'm putting, like, when I want to look at something, and when we talked about, uh, 10% of the population, we always ask where they are. And this is, um, how, with the, uh, abilities of AI, in the future, we can do more about reach the unreachable. Meaning, like, how do we not just look at those people who are under our, uh, you know, very easy to track pathway, uh, but more of those people who are not really out there and we cannot offer them what we offer. People who are totally out, uh, of the closet can do whatever they want. And it's. For example, uh, PREP is a good example, pre exposure prophylaxis. Because if you want to actually get prep today, you have to go to your primary care saying, hi, uh, I'm Roy, I'm gay. And it's not just that I'm just gay. I'm also having sex without condoms. Now I heard about this thing called prep, and I want it. And you should think about how many barriers you have just to say that and then circle of getting prep, which is. And that's why even Israel is a total failure. By the way, with prep, it was, uh, until now, it didn't reach any point that it decreased the number of, of new infections, which is terrible. Really? Yeah. Unlike New York, for example, where something happened there. Like when in 12. In 2012, prep was implemented in New York, you started to see the decrease in the number of new infections. But then today we are in a plateau and still there are a lot of new infections. Now why is that? Uh, because again, the ones who got prep eventually are, uh, the white privileged People like, I'm, I'm saying it as a slogan. Yeah, it's not white privilege, but more the people who can offer who are out of the closet. A lot of them were using condoms before and this was a good way to enhance their letting go condoms and the condom fatigue era. Um, and those ones who are Latinos, African Americans, all of those are still in the same problem because they have to go to the physician and ask for prep. And if you are bundling between having prep and being gay, it will never change. That's why I'm saying that I think like the future is that prep is for everyone and it doesn't say anything about you. And if we are there, that might do a change because, you know, it might be with the women, with men, we don't care. You think you need it, get it. That's it.

Speaker A: Yeah, that's a good point. Yeah. And I know, you know, at least from New York, I know that, you know, they uh, uh, first of all from the like economic barrier, they had programs m that offered it for free so that lower socioeconomic, you know, people could, could get it. But I think, yes, like you said, there's still, there's always going to be that cultural barrier of like, what does that actually mean? Right. If you're down on the DL, if you're not out, if you don't want to talk to your doctor, that's sort

Speaker B: of that, that sometimes you don't want to talk with yourself, by the way.

Speaker A: You're not even. You're not to admit it to yourself. Absolutely. I think where AI can be very helpful is to also identify, like you said, all those different behaviors that you had in the system in terms of what exams they do and what, what medicines they take, et cetera. There are other behaviors that are probably, you know, could create patterns that only not the human eye would not be able to see, but AI could find those patterns, um, that could be helpful in identifying these people and trying to engage with them in advertising. That is a very dangerous thing and it becomes creepy. But I think that in medicine it probably is much more helpful.

Speaker B: Yeah, I wish in a, you know, in a perfect world all what we just talked about would be used for a good causes.

Speaker A: Yes.

Speaker B: Reasons and for the better of all. But unfortunately, in the world we are in, um, I think, uh, the main message is that we always have to balance between this, uh, privacy issues that might be used against us one day.

Speaker A: And I also want to point out, just separately that, you know, I follow you on LinkedIn. You have really great posts about, like you said, harm reduction and different content about LGBTQ health. So I recommend that to anybody. And so speaking of that, if, uh, you know, anybody's interested in reaching out, contacting you, or where, where can they find you?

Speaker B: So I'm, uh, first of all, I'm here in Tel Aviv most of the time, although I'm traveling quite a lot. And, um, and except of that, I'm accepting still patients, um, either privately or in Ganmair Clinic, which is the Tel Aviv Clinic, the LGBT Tel Aviv Clinic in Tel Aviv. And, uh, generally in social media, I'm very out there, either Instagram, Facebook. Someone is using Facebook. I'm not sure anymore. And, uh, it's a certain, uh, age niche.

Speaker A: Yes.

Speaker B: Um, not the kids and LinkedIn, like in the, in the professional part, I'm very active in LinkedIn. I really believe that it's a great way to have a different kind of change in social media towards professionals, and that's why I'm putting quite a lot of efforts there.

Speaker A: Well, you do amazing work and it's very appreciated. And, uh, I recommend anybody take a look at your LinkedIn and follow you because you put out great content. Dr. Tuka, thank you so, so much for coming on the show and talking about all of this.

Speaker B: Thank you for having me.

Speaker A: Well, thanks for joining us today and listening to this episode. Please remember to subscribe, rate and review our podcast, and if you have any questions you like addressed, send them to who's your dadanaowmail? Ah dot com. That's whoseyourdata Now. All one word. Uh, Gmail dot com. Thanks and see you next time on who's your Data.