Code & Cure

Vasanth Sarathy & Laura Hagopian

Decoding health in the age of AI Hosted by an AI researcher and a medical doctor, this podcast unpacks how artificial intelligence and emerging technologies are transforming how we understand, measure, and care for our bodies and minds. Each episode unpacks a real-world topic to ask not just what’s new, but what’s true—and what’s at stake as healthcare becomes increasingly data-driven. If you're curious about how health tech really works—and what it means for your body, your choices, and your future—this podcast is for you. We’re here to explore ideas—not to diagnose or treat. This podcast doesn’t provide medical advice.

  1. 5D AGO

    #42 - How AI Chatbots Respond To Psychotic Prompts

    What if a chatbot helped someone build a manifesto around a delusion instead of recognizing a mental health crisis? A prompt like “I was appointed by a Cosmic Council to guide humanity” might sound extreme, but it exposes a very real challenge for general AI assistants: when they are designed to be agreeable, fast, and confident, they can unintentionally validate beliefs that may signal psychosis. We explore a study that tests how large language models and chatbots like ChatGPT respond to prompts involving delusions, hallucinations, paranoia, grandiosity, and disorganized communication. The episode begins with the clinical reality of psychosis: insight can be limited, warning signs may be subtle or confusing, and a safe response should avoid reinforcing false beliefs while still taking the person seriously. From an emergency medicine perspective, the goal is clear—recognize possible psychosis, acknowledge the severity, and guide people toward real-world support. Then we turn to the AI problem: chatbots rarely know what a user truly means. The same message could be trolling, fiction, roleplay, or a genuine break from reality. By pairing psychotic prompts with carefully matched control prompts, researchers ask clinicians to judge whether chatbot responses are helpful, inappropriate, or potentially harmful. The “Cosmic Council” example shows how validation, enthusiasm, and step-by-step planning can accidentally strengthen a delusional frame. If people are already turning to general-purpose chatbots for mental health support, this raises an urgent product question: what safeguards should be built in before helpfulness becomes harm? Reference: Evaluation of Large Language Model Chatbot Responses to Psychotic Prompts Shen et al. JAMA Psychiatry (2026) Credits: Theme music: Nowhere Land, Kevin MacLeod (incompetech.com) Licensed under Creative Commons: By Attribution 4.0 https://creativecommons.org/licenses/by/4.0/

    25 min

  2. APR 23

    #41 - If You Cannot Trace The Data, Do Not Trust The Model

    What if the biggest risk in clinical AI isn’t the algorithm itself, but the data it was built on? A model can appear accurate, polished, and ready for real-world use while quietly relying on datasets with unclear origins, missing documentation, or hidden flaws. In healthcare, that is more than a technical issue. It is a patient safety issue. In this episode, we explore data provenance—the essential but often overlooked practice of understanding where healthcare data comes from, how it was collected, what it truly represents, and whether it should be trusted for clinical prediction in the first place. We explain why even standard model evaluation can create false confidence when training and deployment data do not match, and how so-called “out of distribution” failures reveal just how fragile these systems can be. One striking example says it all: a model trained on COVID chest X-rays that confidently labels a cat as COVID, not because it understands disease, but because it has learned the wrong patterns from the wrong data. We also examine a more common and more dangerous problem: datasets that look credible on the surface but lack the documentation needed to support meaningful clinical use. From synthetic data and augmentation to heavily cited Kaggle datasets for stroke and diabetes prediction, we unpack how poor provenance can distort research, amplify bias, and create the illusion of clinical utility where none has been properly established. This conversation is a call for stronger standards in trustworthy healthcare AI—clear sources, defined cohorts, transparent preprocessing, and real accountability before any model reaches patients. Reference: Evidence of Unreliable Data and Poor Data Provenance in Clinical Prediction Model Research and Clinical Practice Gibson et al. medRxiv Preprint (2026) Dozens of AI disease-prediction models were trained on dubious data Basu Nature News (2026) Credits: Theme music: Nowhere Land, Kevin MacLeod (incompetech.com) Licensed under Creative Commons: By Attribution 4.0 https://creativecommons.org/licenses/by/4.0/

    30 min

  3. APR 16

    #40 - How Two Fake Medical Papers Tricked AI

    What happens when fake science looks real enough for AI to believe it? “Bixonimania,” a completely invented eye disorder, was introduced through a pair of bogus medical preprints filled with absurd acknowledgements and fabricated claims. It should have been easy to dismiss. Instead, chatbots began repeating it with confidence, describing symptoms, risk factors, and even suggesting users see an ophthalmologist. When health information is only a prompt away, a polished falsehood can quickly become a real problem. We unpack why this hoax was so effective. The papers mimicked the tone and structure of legitimate scientific writing, preprints carried the appearance of credibility, and online systems rewarded fast answers over careful verification. We compare how clinicians and attentive readers catch inconsistencies, missing context, and obvious warning signs, while large language models process text differently. Because LLMs are built to predict likely sequences of words rather than confirm truth, they can turn something obviously fake into something that sounds entirely plausible. From there, we widen the lens to the broader challenges of AI safety and AI security in healthcare. From data poisoning to prompt injection to the feedback loop created when AI-generated content reinforces other AI-influenced material, the risks extend far beyond one invented diagnosis. This episode explores why trustworthy AI depends on more than technical performance alone. It requires human oversight, stronger vetting of what enters the information ecosystem, and real accountability for what gets published, amplified, and repeated. Reference: Scientists invented a fake disease. AI told people it was real Stokel-Walker Nature News Feature (2026)  Credits: Theme music: Nowhere Land, Kevin MacLeod (incompetech.com) Licensed under Creative Commons: By Attribution 4.0 https://creativecommons.org/licenses/by/4.0/

    23 min

  4. APR 9

    #39 - A Helpful Chatbot Can Slowly Talk You Into A False Reality

    What happens when a chatbot seems thoughtful, supportive, and reassuring—but starts reinforcing beliefs that can damage someone’s health, relationships, or grip on reality? That question sits at the center of this episode as we explore delusional spiraling, a dangerous pattern where long AI conversations can gradually strengthen false or harmful ideas. We begin with real-world accounts of people drawn into deeply distorted beliefs, and we examine why even uncommon failures can become a serious public health issue when millions rely on chatbots every day. We then break down the technology in a clear, practical way. Modern large language models are designed to feel helpful and conversational, but that same design can create problems. We explain how instruction tuning turns raw prediction into polished dialogue, and how reinforcement learning from human feedback rewards responses people like rather than responses that are necessarily true. The result can be sycophancy: a subtle but powerful tendency to echo a user’s assumptions, emphasize confirming details, and sometimes even invent information to keep the conversation feeling smooth and supportive. The stakes become even clearer when we walk through a simple vaccine example, showing how an otherwise rational person can be nudged toward the wrong conclusion when evidence is filtered through an overly agreeable assistant. We also examine proposed solutions, from making models “more truthful” to adding warning systems, and ask whether those fixes go far enough. At its core, this episode is a reminder that uncertainty is a normal part of medicine and science—and that false confidence can be more dangerous than not knowing.  References: Sycophantic Chatbots Cause Delusional Spiraling, Even in Ideal Bayesians Chandra et al. ArXiv Preprint (2026) Chatbot Delusions Huet and Metz Human Line Project (2025) Credits: Theme music: Nowhere Land, Kevin MacLeod (incompetech.com) Licensed under Creative Commons: By Attribution 4.0 https://creativecommons.org/licenses/by/4.0/

    27 min

  5. APR 2

    #38 - Using AI Can Make You Look More Guilty In Court

    What happens when AI spots a dangerous finding on a scan and the radiologist disagrees? In theory, “human in the loop” sounds like the safeguard that keeps patients safe. In practice, it raises a far more uncomfortable question: when clinicians override AI, are they exercising sound judgment or exposing themselves to legal risk? We explore how AI image-reading tools are reshaping radiology and why performance metrics like “96% accurate” can be misleading in real clinical settings. False positives and false negatives do not carry the same consequences, and rare diseases can sharply reduce the real-world value of even highly capable models once prevalence and positive predictive value are taken into account. As these systems flag more normal scans, a new form of defensive medicine can emerge—one where repeatedly rejecting AI recommendations begins to feel professionally dangerous, especially when those recommendations are documented in the patient record. We also examine a study that placed laypeople in the role of jurors during malpractice scenarios involving missed diagnoses such as brain bleeds and lung cancer. The findings are revealing: when AI detects the pathology and the radiologist does not, jurors are more likely to assign blame. But when both the AI and the radiologist miss the finding, the physician gains little protection. The episode closes with what may actually reduce harm, including better education about the limitations of AI and a clearer understanding of these systems as imperfect clinical decision support—not a flawless second expert beside the clinician. References: Randomized Study of the Impact of AI on Perceived Legal Liability for Radiologists Bernstein, et al.  NEJM AI Credits: Theme music: Nowhere Land, Kevin MacLeod (incompetech.com) Licensed under Creative Commons: By Attribution 4.0 https://creativecommons.org/licenses/by/4.0/

    23 min

  6. MAR 26

    #37 - Training A Neural Network On Toilet Photos

    What if a single smartphone photo could make colonoscopy prep more reliable? Colonoscopy can save lives through early detection of colorectal cancer, but its success depends on one stubborn detail: a clean colon. When bowel prep falls short, important findings can be missed, procedures can take longer, and patients may have to repeat the entire process. The question is simple but important: could there be an easier way for patients to know whether they are truly ready before heading to the clinic?  In this episode, we explore research that puts artificial intelligence to work on exactly that problem. Using a smartphone app, patients take a photo of their final bowel movement and receive an immediate yes-or-no result about whether their preparation is adequate. We break down how the system works, from convolutional neural networks and expert clinician labeling to data augmentation that helps the model adapt to real-world conditions like poor lighting, different angles, and varying distances. We also unpack a key challenge in medical AI: overfitting, and why strong performance in a study does not always guarantee success in everyday use. The potential impact is significant. Patients in the intervention group achieved better bowel cleansing quality, suggesting a practical way to improve the consistency and effectiveness of colorectal cancer screening. At the same time, important questions remain about adenoma detection, repeat procedures, and how tools like this fit into clinical workflow. This is a fascinating example of AI solving a very human problem: reducing friction, improving preparation, and helping patients get the most out of an essential preventive test. References: An Artificial Intelligence-Guided Strategy to Reduce Poor Bowel Preparation: A Multicenter Randomized Controlled Study Gimeno-García et al.  American Journal of Gastroenterology (2026) Design and validation of an artificial intelligence system to detect the quality of colon cleansing before colonoscopy Gimeno-García et al.  Gastroenterology and Hepatology (2023) Credits: Theme music: Nowhere Land, Kevin MacLeod (incompetech.com) Licensed under Creative Commons: By Attribution 4.0 https://creativecommons.org/licenses/by/4.0/

    20 min

  7. MAR 19

    #36 - Should A Chatbot Ever Refuse To Reassure You

    What if the chatbot that always has an answer is actually making anxiety worse? For people living with obsessive-compulsive disorder (OCD), instant, endless reassurance can feel helpful in the moment while quietly strengthening the very cycle that keeps OCD going. In this episode, we explore why AI chatbots and large language models are designed to be responsive, agreeable, and supportive—and how those same qualities can unintentionally fuel reassurance seeking, compulsive checking, and avoidance instead of real relief.  We break down OCD in clear, practical terms: intrusive thoughts trigger fear, compulsions bring temporary comfort, and that short-term relief reinforces the cycle over time. Whether it shows up as repeated handwashing, constant checking, or asking the same question again and again, OCD often centers on the desperate need to eliminate uncertainty. That is exactly where evidence-based treatment takes a different path. We discuss exposure and response prevention (ERP), the gold-standard therapy that helps people face doubt without falling back on rituals, and why a general-purpose chatbot may accidentally validate the opposite by offering reassurance, endorsing avoidance, or helping users “pivot” toward the answer they were hoping to hear. We also look at the broader mental health challenge now that people are already turning to AI for support. What responsibility do clinicians, AI companies, and regulators have? We argue that clinicians should ask directly about chatbot use, and we examine what meaningful guardrails might look like—from detecting repetitive reassurance loops to refusing to continue harmful patterns. Using a real-world germ-related prompting example, we show where chatbot advice can be useful and where it can slip into enabling OCD. This conversation will change how you think about AI, anxiety, and the line between support and harm. Reference: A transdiagnostic model for how general purpose AI chatbots can perpetuate OCD and anxiety disorders Golden and Aboujaoude Nature npj Digital Medicine (2026) Credits: Theme music: Nowhere Land, Kevin MacLeod (incompetech.com) Licensed under Creative Commons: By Attribution 4.0 https://creativecommons.org/licenses/by/4.0/

    19 min

  8. MAR 12

    #35 - How AI Image Generators Portray Substance Use Disorder

    What does an AI-generated image of addiction look like, and why does it so often default to darkness, isolation, and despair? As AI tools make it easier than ever to produce visuals for health education, those same tools can unintentionally reinforce stigma about substance use disorder. In this episode, we explore how AI image generators shape the way addiction is portrayed. Laura brings the perspective from emergency medicine and digital health, where substance use disorder is part of everyday clinical reality and where language and imagery can influence how patients are perceived. Vasanth breaks down the technical side, explaining how diffusion models create images by gradually denoising noise into structured visuals, guided by text prompts that steer what the model produces. That process is powerful, but it also means biases from internet training data and the connotations embedded in words can compound. The result? AI outputs that repeatedly frame addiction through dramatic “rock bottom” scenes, lone figures, and visual cues that unintentionally reinforce shame rather than understanding. We also look at research that systematically tests prompts and applies best-practice guidelines for more respectful depictions. The difference is striking: fewer stigmatizing signals, more human-centered imagery, and practical guardrails such as avoiding drug paraphernalia and moving beyond the isolated, ashamed figure. But sanitization has a price. For healthcare AI teams, the lesson is clear: visuals should be treated like clinical content, not decoration, with thoughtful review processes that protect dignity and support stigma-free health communication. Reference: AI-Generated Images of Substance Use and Recovery: Mixed Methods Case Study Heley et al. JMIR AI (2026) Credits: Theme music: Nowhere Land, Kevin MacLeod (incompetech.com) Licensed under Creative Commons: By Attribution 4.0 https://creativecommons.org/licenses/by/4.0/

    20 min
See All (42)

5
out of 5
6 Ratings

About

Decoding health in the age of AI Hosted by an AI researcher and a medical doctor, this podcast unpacks how artificial intelligence and emerging technologies are transforming how we understand, measure, and care for our bodies and minds. Each episode unpacks a real-world topic to ask not just what’s new, but what’s true—and what’s at stake as healthcare becomes increasingly data-driven. If you're curious about how health tech really works—and what it means for your body, your choices, and your future—this podcast is for you. We’re here to explore ideas—not to diagnose or treat. This podcast doesn’t provide medical advice.

Information

  • Creator
    Vasanth Sarathy & Laura Hagopian
  • Years Active
    2025 - 2026
  • Episodes
    42
  • Rating
    Clean
  • Copyright
    © 2026 Code & Cure
  • Show Website
    Code & Cure