The AI Revolution in Medicine Isn't Coming - It's Already Here

In this engaging episode of Heliox, where evidence meets empathy, hosts dive deep into the fascinating world of generative AI in medicine. Through analysis of a comprehensive JMIR study examining over 5,000 research articles, they explore how AI is transforming healthcare - from clinical decision support and medical education to patient care and surgical planning. The discussion brings AI applications to life through real-world examples, including Dr. Carter's pioneering work in heart failure prediction and Dr. Lee's use of AI in surgical planning. While celebrating the incredible potential of AI in healthcare, the hosts thoughtfully consider the importance of responsible implementation and human oversight. Whether you're a healthcare professional, tech enthusiast, or simply curious about the future of medicine, this episode offers valuable insights into how AI is reshaping the medical landscape.

Tracking knowledge evolution, hotspots and future directions of generative artificial intelligence in medicine: A bibliometric and visualized analysis
https://preprints.jmir.org/preprint/70258

@jmirpub.bsky.social

https://bsky.app/profile/jmirpub.bsky.social/post/3ldonpvmyry2d

This is Heliox: Where Evidence Meets Empathy

Independent, moderated, timely, deep, gentle, clinical, global, and community conversations about things that matter.  Breathe Easy, we go deep and lightly surface the big ideas.

Thanks for listening today!

Four recurring narratives underlie every episode: boundary dissolution, adaptive complexity, embodied knowledge, and quantum-like uncertainty. These aren’t just philosophical musings but frameworks for understanding our modern world. 

We hope you continue exploring our other podcasts, responding to the content, and checking out our related articles on the Heliox Podcast on Substack. 

Transcript

0:24

Welcome back to the Deep Dive. Today, we're diving into the world of generative AI in medicine. We've got a ton of research to unpack. A really interesting study from JMR Preprints analyzed over 5,000 research articles on the topic. It's incredible how much this field has exploded, going from just a handful of publications in 2018 to over 5,000 by the end of 2024. It's really remarkable. It's not just the sheer number of publications, though. It's the speed of the growth. It points to a massive shift in how we're thinking about the role of AI in healthcare. Yeah, for sure. It's not just hype either. We're seeing real-world applications. The study actually breaks down the research into 12 key areas, everything from clinical decision support to patient education. We're talking AI that can help doctors diagnose diseases- More accurately. More accurately. Personalized treatment plans. Even create realistic simulations for training. It's pretty amazing stuff. It is. What I find really interesting is the geographic distribution of the research. The US and China are clear leaders, but the UK punches way above its weight in terms of impact. Their research is getting cited more often than any other country. That's interesting. Why do you think that is? Well, it suggests that maybe they're focusing on different areas or approaches that are proving particularly influential. Okay. I'm curious about this concentration of research in the US and China. Do you think it's just a matter of funding or are there other factors at play? I think it's likely a combination of things. Both countries have invested heavily in AI research and development. They also have large healthcare systems that provide tons of data for AI models to learn from. Makes sense. China in particular has a more centralized approach to data collection, which can be a big advantage for AI development. Yeah, definitely. But funding and data aren't the whole story. There's also a cultural element. Oh, interesting. The US and China have embraced this culture of innovation and rapid technological adoption, which has likely sped up the growth of AI in medicine. I see what you mean. So maybe countries like the UK with their high citation rate are taking a more focused strategic approach, prioritizing quality over quantity. That's a really good point. The UK's research might be more targeted towards fundamental breakthroughs or addressing very specific clinical needs, leading to higher impact within the research community. Yeah, that makes sense. Okay, so let's dive into those 12 keyword clusters the study identified. Each one represents a key area where generative AI is being applied in medicine. And we're talking everything from clinical decision support, which could completely revolutionize how doctors diagnose and treat diseases, to patient education, which could empower individuals to take a more active role in their own health. Yeah, it's a broad spectrum of applications. It is. And we're seeing AI being used to create these incredibly realistic simulations for medical training, potentially transforming how we educate future healthcare professionals. That's really cool. The study highlights the cluster medical education as having the strongest citation burst. It seems like this area is attracting a lot of attention and generating some groundbreaking research. Absolutely. The ability to create these personalized and interactive learning experiences, it has huge potential. It's definitely an area where researchers are excited. Yeah, I can see why. And speaking of exciting applications, we can't ignore the elephant in the room, chatGPT. It seems like every day there's a new story about how it's being used in medicine. The study points to several high-profile research articles exploring chatGPT's capabilities, from helping with rhinoplasty consultations to supporting really complex medical discussions. It's pretty wild. ChatGPT and large language models in general are so fascinating because they can process and generate human-like text. This opens up so many possibilities for patient communication, summarizing medical literature, all sorts of things. It's true. It seems like some researchers are even using chatGPT to help them come up with research questions. Yeah, it's pretty meta. It is. But let's get into some specifics. The study mentions some really interesting examples of chatGPT in action. Can you walk us through a few of those? Sure. One example that comes to mind is using chatGPT to support breast tumor board discussions. These are multidisciplinary meetings where doctors discuss complex cancer cases and make treatment decisions. And researchers found that chatGPT could accurately summarize key points from patient records and medical literature, potentially streamlining these discussions and making sure that all the relevant information is considered. Wow. That's a pretty high-stakes application. Did the researchers have any concerns about accuracy or potential biases in how chatGPT might summarize that information? Yes, definitely. And this highlights a really important point. With any AI system in healthcare, the need for careful validation and oversight. Researchers are constantly evaluating the accuracy of these tools and working to identify and mitigate potential biases. That's good to hear. What about other applications? The study mentioned chatGPT being used for rhinoplasty consultations. How does that even work? Is chatGPT giving patients medical advice? Well, it's important to emphasize that chatGPT is not replacing human doctors. In the case of those consultations, it was used as a tool to provide patients with information about the procedure, potential risks and benefits, and to answer some frequently asked questions. Essentially acting as an interactive information source. So more like an AI-powered patient education tool than a doctor in a box. Exactly. And this highlights another key theme we're seeing, using AI to empower patients. By providing access to clear and personalized information, AI tools can help people make more informed decisions about their health. I like that. This is all incredibly exciting. But I know there are also concerns about the potential downsides of AI in medicine. The study mentioned several key challenges, data security, the potential for misuse, and the need for clear ethical guidelines. Yeah, these concerns are completely valid. It's crucial that we approach the development and deployment of AI in healthcare with a strong focus on responsibility and transparency. Absolutely. But before we get into the potential risks, let's take a closer look at some of the specific applications within those 12 keyword clusters. That's where things get really interesting. And that's what we'll be exploring in part two of this deep dive. Looking forward to it. Me too. Thank you to everyone who has left such positive reviews on our podcast episodes. It helps to make the podcast visible to so many more people. We read them all. Back to Heliox, where evidence meets empathy. Welcome back to our deep dive into generative AI in medicine. So last time we were talking about those 12 keyword clusters that the JMIR preprints study identified. I'm really interested to explore some of these clusters in more detail. Yeah, let's do it. Okay. Let's start with clinical decision support. This is one of the areas where AI's potential is truly transformative. Imagine a world where doctors have AI assistants that can analyze huge amounts of data, you know, patient records, medical literature, the latest research, to help them make faster and more accurate diagnoses. That sounds incredible. But isn't there a risk of doctors becoming too reliant on AI and losing some of their critical thinking skills? That's a valid concern. And it's one that researchers are thinking about a lot. The goal is not to replace doctors, but to augment their capabilities. Think of it like a really advanced tool that can help doctors process information more efficiently and consider a wider range of possibilities, leading to better decisions for patients. So it's more like a partnership between human expertise and AI's analytical power. Exactly. The best outcomes will come from a synergistic approach, where AI is assisting doctors, not dictating to them. Right. That makes sense. So what are some specific examples of how AI is being used for clinical decision support? Well, one area where we're seeing a lot of progress is medical imaging. AI algorithms can analyze scans, like x-rays, CT scans, and MRIs, to detect subtle abnormalities that a human eye might miss. Wow. This can be really helpful for early detection of cancer or other diseases, which can lead to earlier intervention and hopefully better outcomes. That's amazing. It's like having an AI radiologist working alongside the human team. In a way, yes. But it's important to remember that AI is not perfect. These algorithms are trained on massive data sets, but they can still make mistakes. Right. So human oversight is crucial. So it's about using AI to enhance human capabilities, not to replace humans completely. Exactly. Okay. That makes sense. So what about other areas beyond medical imaging? Where else is AI having an impact on clinical decision support? Another exciting application is in personalized medicine. Imagine an AI that can analyze your individual genetic makeup, your medical history, your lifestyle, even environmental factors, to create a treatment plan that's tailored just for you. That's the dream, isn't it? Treatment plans that are truly personalized. Is that actually happening now, or is that more of a future vision? Well, we're seeing the early stages of this personalized approach being implemented. For example, in cancer treatment, AI is being used to analyze tumor genetics, to help doctors choose the most effective therapies for each patient. This is leading to more targeted treatments with fewer side effects. That's incredible. Seems like AI is really starting to deliver on its promise to revolutionize healthcare. Okay, let's switch gears a bit and talk about another key cluster. Patient education. How is AI changing the way patients learn about their health and manage their conditions? Think about all the information patients are bombarded with, from doctor's appointments to online searches. It can be overwhelming. AI can help by creating personalized educational materials that are tailored to each patient's needs and understanding. So instead of just getting a generic pamphlet, patients could get an interactive AI-powered guide that explains their condition in a way that's easy to understand. Exactly. These AI tools can consider things like a patient's age, literacy level, cultural background, even their preferred learning style to create truly personalized educational experiences. That's a game changer, especially for patients with complex conditions, who need to understand a lot of information to manage their health effectively. And it goes beyond just providing information. AI-powered apps can offer patients ongoing support and guidance, helping them track their symptoms, manage their medications, and connect with online communities of people with similar conditions. It's like having a virtual health coach in your pocket. Exactly. And this empowerment can lead to better adherence to treatment plans, improved self-management of chronic conditions, and ultimately better health outcomes. It sounds like AI could really empower patients to take a more active role in their own health care. Absolutely. Okay, before we move on to other clusters, I want to circle back to something you mentioned earlier, the importance of human oversight with AI. Are there specific examples of how this oversight is being implemented in the real world? Yes, there are several approaches being taken to make sure AI is being used responsibly. For example, in radiology, many hospitals have systems in place where a human radiologist always reviews the AI's findings before making a final decision. It's like a double-check system. Exactly. It's about ensuring accuracy and preventing potential errors. So the AI is a tool to help the human expert, not to replace them. Precisely. And this human-in-the-loop approach is being used in lots of areas of AI and medicine. It's about finding the right balance between leveraging AI's capabilities and maintaining human control and responsibility. Right. Okay, let's move on to another interesting cluster, academic integrity. I'm curious to hear more about how AI plays into this. Sure. This cluster highlights both the potential and the challenges of AI tools like ChatGPT and academic settings. On the one hand, these tools can be really helpful for students and researchers. They can help with writing, research, even brainstorming ideas. So they can be powerful tools for learning and knowledge creation. Absolutely. But there's also the potential for misuse. Students could use these tools to plagiarize or to generate content without really understanding the material. So this raises some ethical questions about how to ensure academic integrity in the age of AI. Yeah, it's a double-edged sword. So how are educators and institutions addressing this challenge? Well, many universities are developing guidelines and policies for using AI tools responsibly in academic work. They're also exploring ways to integrate these tools into the curriculum in a way that promotes learning and critical thinking, rather than just providing a shortcut to producing content. It sounds like a tricky balance to strike. It is. Okay. So we want to harness the power of these tools, but also make sure they're being used ethically and responsibly. Exactly. And this is where the cluster social media comes in. Oh, yeah. Social media platforms are a major source of health information these days. And AI is playing a role in shaping how that information is shared and consumed. Right. But isn't there a risk of misinformation spreading quickly on social media? How can we make sure AI is being used to promote accurate and reliable health information? That's a really important concern. And researchers are looking into ways to use AI to detect and flag misinformation on social media platforms. Okay. They're also working on AI-powered tools that can help users evaluate the credibility of health information they find online. So it's about using AI to combat misinformation and to help users be more critical of the health information they come across. Exactly. This conversation has really highlighted how broadly AI is impacting medicine. From clinical decision support to patient education, it seems like no area of health care is untouched by this technological revolution. And we've only just scratched the surface. As AI technology keeps advancing, we can expect even more innovative and transformative applications to emerge. It's an exciting time to be following this field. But with all the excitement, it's important to remember that AI is a tool. And like any tool, it can be used for good or for bad. Absolutely. We've talked about the ethical considerations. And it's something we need to keep in mind as AI becomes more integrated into health care. Transparency, accountability, and human oversight are all essential. Yes. We need to make sure that AI is used responsibly and ethically, with the well-being of patients always coming first. AI should enhance human capabilities, not replace human judgment. It's about finding the right balance, using AI to improve health care, while upholding the values that make health care truly human. A shout out to our many listeners in Gibsons, Sechelt, Melbourne, Helsinki, New Orleans, Vancouver, Singapore, Copenhagen, and Sydney. We see you. Thank you for subscribing, following, commenting, and supporting our podcast. Find related articles at Heliox Podcasts on Substack. Back to Heliox, where evidence meets empathy. Welcome back to the Deep Dive. We spent the last two episodes exploring the incredible world of generative AI in medicine. We've talked about the research landscape, those 12 fascinating keyword clusters, and the ethical considerations. Now it's time to see generative AI in action. Let's meet the people who are using it to change health care right now. Sounds good. Okay. Let's start with Dr. Emily Carter. She's a cardiologist at a leading research hospital, and she's using AI to predict heart failure risk in her patients. Wow. That sounds like something straight out of science fiction. How does that even work? Is it some kind of futuristic crystal ball? Not quite a crystal ball, but the technology is pretty amazing. Dr. Carter is using an AI model that analyzes a patient's medical history, lifestyle factors, and even genetic data. To identify patterns that suggest an increased risk of heart failure. So it's about catching those warning signs early. Exactly. Early detection is key with heart failure. By identifying high-risk patients, Dr. Carter can intervene early with lifestyle changes, medications, or other therapies that can prevent or delay the disease. It's like preventive medicine taken to a whole new level. What do Dr. Carter's patients think about this technology? Are they comfortable with AI playing such a big role in their health care? Dr. Carter says most of her patients are really excited about the potential of AI. They understand that it can help her make more informed decisions and provide more personalized care. Of course, some patients are always a bit hesitant about new technology, and it's important to address those concerns and explain how AI is being used responsibly. Makes sense. It sounds like Dr. Carter is really a pioneer in using AI to improve patient care. Are there other doctors out there using generative AI in similar ways? Absolutely. Dr. David Lee, he's a surgeon specializing in minimally invasive procedures, and he's using AI to improve surgical planning and precision. Okay, now I'm picturing some kind of robotic arm-performing surgery with superhuman accuracy. Well, it's not quite that futuristic, but Dr. Lee's using AI to create 3D models of his patients' anatomy. Based on their scans, these models let him plan the surgery in incredible detail, so he can identify the best approach and minimize the risk of complications. So it's like having a virtual blueprint of the patient's body. Exactly. This level of detail and precision is especially important for minimally invasive procedures, where surgeons are working in very tight spaces. Dr. Lee says AI has significantly improved his ability to perform these complex surgeries with greater safety and effectiveness. That's incredible. It's amazing to see how AI is transforming the operating room. But we've also talked about AI's potential to empower patients. Are there any real-world examples of that? Definitely. Sarah Jones is a great example. She's a patient advocate living with diabetes, and she uses an AI-powered app to manage her condition. Interesting. The app tracks her blood sugar levels, her activity, and her diet, and then it gives her personalized recommendations to stay healthy. So it's like having a virtual health coach right on your phone. Exactly. And Sarah says it's not just about getting information. The app has helped her understand her condition better, make healthier choices, and feel more in control of her health. It's amazing how AI can give patients the tools to be more proactive about their health. These real-world examples really show how generative AI is making a difference in medicine. It's not just about some far-off future. It's happening now. It is. And remember, these are just a few examples. There are so many ways that generative AI is being used in health care, from drug discovery to mental health support. It's such an exciting field to be following. But with all the excitement, it's crucial to remember that AI is just a tool. And like any tool, it can be used for good or bad. That's true. We've talked about the ethical implications, and that's something we need to think about carefully as AI becomes more a part of health care. Yeah, transparency, accountability, and human oversight are all essential. Absolutely. We need to make sure AI is used ethically and responsibly, always putting the well-being of patients first. AI should be there to help us, not to replace human judgment. Right. It's about finding that balance, using AI to improve health care, while still holding on to the values that make health care human. Well, we've covered a lot of ground in this deep dive, exploring both the amazing potential of AI in medicine and the challenges that come with it. Now it was your turn to think about what you've heard. What do you think about the role of AI in health care? Stay informed, ask questions, and be part of the conversation about how we want AI to shape the future of health care. The future is being shaped right now, and we all have a role to play in making sure AI is used to create a healthier and more equitable world for everyone. Thanks for joining us on the Deep Dive.