Code & Cure

Vasanth Sarathy & Laura Hagopian
  • 5.0 (6)
  • Health & Fitness

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

    #53 - Pretty Pictures, Dangerous Mistakes

    What happens when the picture that's teaching you medicine was never real in the first place? AI image generators can now produce custom anatomy diagrams, exam findings, and procedure illustrations on demand — and a single convincing visual can shape how a future clinician diagnoses, treats, and even what they believe "normal" looks like. We break down why the stakes are so high in medical education: medicine is deeply visual, and tailored images could genuinely help students learn anatomy, physical exams, imaging, and procedures faster. But a new systematic review of 36 studies finds two problems hiding behind the polish — representational bias, with clinicians depicted as overwhelmingly white and male, and clinical fidelity failures in nearly half the studies reviewed. We run our own test case, asking a model for an orthopedic surgeon placing an ulnar gutter splint for a boxer's fracture — and getting an image that looks flawless while being anatomically and procedurally wrong. Then we turn to why this happens: how diffusion models generate images by denoising toward "plausible," why their training rewards looks-right over is-right, and how web-scraped datasets, image compression, and underspecified prompts add up to confident errors at scale. For anyone interested in patient safety, algorithmic bias, or the future of AI in medical training, this episode is about a skill every clinician now needs — knowing when to trust an image, and when to stop and verify. References: Bias, representation, and clinical fidelity in AI-generated images for medical education: a systematic literature review Alon et al. 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/

  2. Jul 9

    #52 - When "Once A Day" Becomes Eleven Pills

    What if medical AI looks unstoppable right up until you change the language? One year into Code & Cure, we pull on an unsettling thread: a model can score around 90% on an English medical exam and then crash to about 55% on the same exam in French, with similar drops across other languages. That should give us pause—healthcare doesn't happen in a single language, and patient safety can't ride on English-only competence. We dig into why this happens by putting human clinicians next to large language models. A doctor doesn't become "less medical" when they switch to Spanish or French; fluency shapes how smoothly they communicate, not what they know. LLMs work differently. They learn by predicting the next token from the data they see most, so when English dominates training, the patterns—and the medical "knowledge" riding inside them—are strongest in English. In lower-resource languages the patterns are thinner, and the model's apparent reasoning can fall apart even when the question contains everything it needs. Then we take on the popular fix: machine translation. It sounds straightforward until you look at where it actually breaks—numbers, temporal qualifiers, negation, and culture-bound idioms. "Once a day" becoming "eleven times a day" is not a harmless glitch. We also unpack how common translation metrics can reward surface-level word overlap while missing exactly the meaning errors that matter most at the bedside. For anyone building or using clinical AI, the takeaway is hard to dodge: if we want medical AI we can trust, multilingual competence can't be an afterthought. A system that's unsafe outside English shouldn't be called general medical intelligence. References: When medical AI fails outside English Li et al. BMJ Digital Health & 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/

  3. Jul 2

    #51 - The Autonomy Illusion

    What if the feeling of being in control is exactly what's being engineered? We dig into AI paternalism—the quiet ways large language models and recommendation systems can shape human decisions while appearing to serve them. The unsettling part is that it never announces itself. It looks like convenience, speed, and a clean recommendation delivered with total confidence, right when real life feels messy. You still feel like the one deciding. That feeling may be the illusion. We break autonomy into three practical pieces: understanding, competency, and voluntariness. AI can genuinely improve understanding—summarizing medical research, translating dense health information into plain language—but hallucinations, biased training data, missing context, and outdated guidance can quietly swap knowledge for misinformation without anyone noticing. Then we turn to what repeated offloading does to us. When we hand decisions to machines again and again, we risk deskilling—whether that's clinicians leaning on decision support tools or the rest of us navigating life the way we now navigate roads: trusting the GPS and forgetting the map. Voluntariness raises the biggest red flag of all: personalization can nudge, filter, and frame options so that the choice feels free while being steered. For anyone interested in AI in healthcare, patient autonomy, informed consent, or the future of human agency, this episode asks how to tell real autonomy from its imitation—and what it takes to keep your decisions yours. References: Artificial autonomy and algorithmic paternalism: AI shaping human autonomy and decision-making Hofmann Frontiers in Artificial Intelligence (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/

  4. Jun 25

    #50 - AI Caught The Heart Failure Nobody Saw

    What if a five-minute EKG could reveal more than a rhythm problem or heart attack? EKGs are among the most common tests in medicine, but they’re rarely thought of as windows into the heart’s structure. That assumption changes with a remarkable case: a 45-year-old arrives in the ER with cough and trouble breathing, improves with treatment, and seems ready to go home. But an AI model reading the EKG detects something unusual—triggering a deeper workup that uncovers a dangerously weakened heart and ultimately leads to a heart transplant. We break down the medicine in plain language, from what the spikes and waves on an EKG actually mean to what an echocardiogram can show that an EKG usually cannot. Along the way, we explore why structural heart disease can be so difficult to catch early, especially when symptoms don’t follow the classic heart failure script. Then we turn to the technology behind the alert. EchoNext is trained on massive paired datasets of EKGs and echocardiograms, allowing convolutional neural networks to detect subtle patterns across multiple leads that human eyes might miss. But the promise of clinical AI comes with real-world challenges: how much interpretability clinicians need, what tools like saliency maps actually explain, and how false positives can strain healthcare systems through extra scans, staffing needs, and follow-up care. For anyone interested in AI in healthcare, cardiology, patient safety, or what it really takes to deploy medical AI responsibly, this episode connects the math, the medicine, and the messy reality in between. References: A case of artificial intelligence-enhanced diagnostics leading to heart transplantation Hartman et al. Nature Medicine (2026) Detecting structural heart disease from electrocardiograms using AI Poterucha et al. Nature (2026) Deep Learning Electrocardiographic Analysis for Detection of Left-Sided Valvular Heart Disease Poterucha et al. JACC (2022) rECHOmmend: An ECG-Based Machine Learning Approach for Identifying Patients at Increased Risk of Undiagnosed Structural Heart Disease Detectable by Echocardiography Ulloa-Cerna et al. Circulation (2022) Credits: Theme music: Nowhere Land, Kevin MacLeod (incompetech.com) Licensed under Creative Commons: By Attribution 4.0 https://creativecommons.org/licenses/by/4.0/

  5. Jun 18

    #49 - My Robot Ghosted Me And It Hurt

    What happens when the AI companion you rely on simply disappears? For people using mental health chatbots, social robots, or always-on support tools, discontinuation is not just a technical inconvenience. When funding runs out, servers shut down, or companies close, users can lose a system they have built routines, trust, and even emotional connection around. In a mental health context, that abrupt ending can feel like being ghosted—and the consequences can be real. We explore this uncomfortable reality through the story of Jibo, the charming social robot that began as an MIT project and eventually had to say goodbye when the business behind it collapsed. From there, we unpack why people bond with machines in the first place: expressive design, humanlike conversation, anthropomorphism, and the simple fact that something helpful can start to feel like a partner. Research shows that people can become attached not only to social robots, but also to everyday devices and practical tools—raising new questions as large language model chatbots become more empathetic, conversational, and personal. The clinical lesson is clear: endings matter. In human therapy, transitions are handled with care through closure sessions, support planning, and a focus on building independence rather than dependence. We discuss what ethical offboarding for mental health AI could look like, including advance notice, gradual tapering, progress summaries, data portability, and clear pathways to human support. As AI becomes more deeply woven into emotional and clinical care, designing a responsible goodbye may be just as important as designing the first hello. References: Artificial Intelligence Discontinuation Effects (AI-DICE): An Emerging Phenomenon in Mental Health Applications Kelly 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/

  6. Jun 11

    #48 - Good Medicine Starts With Saying I Don’t Know

    What if the most dangerous AI answer is the one that sounds the most certain? We start with a playful challenge about the moon’s diameter, then use it to explore a much bigger question in healthcare: how should AI systems communicate uncertainty instead of simply projecting confidence to the user? We dive into how clinicians make decisions when the facts are incomplete. In the emergency department, documentation workflows, and automated ICD-10 coding, medical reasoning rarely depends on a single perfect answer. Clinicians rank a differential, search for evidence that could prove them wrong, prioritize what is urgent, and bring in specialists when needed. That process is built for uncertainty. Yet many healthcare AI tools, from large language models to traditional machine learning classifiers, are still designed to deliver one “best” answer, even when the situation calls for caution. The episode breaks uncertainty into two practical categories: aleatoric uncertainty, which comes from ambiguity and noise in the data, and epistemic uncertainty, which appears when a case falls outside the model’s knowledge. Along the way, we unpack what probability scores really mean, why near-ties deserve attention, and why out-of-distribution detection matters when a model might confidently mistake the unfamiliar for the known. The key takeaway is simple: safer AI systems do not hide uncertainty. They make it visible, communicate it clearly, and know when to abstain. References: Uncertainty-aware abstention in medical diagnosis based on medical texts Vazhentsev et al. Nature Artificial Intelligence (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/

  7. Jun 4

    #47 - Depression Screening with Digital Phenotypes

    What if depression could be monitored with the same continuity as blood pressure or heart rhythms? While physical health is often tracked visit after visit, depression is still commonly measured through a brief PHQ-9 questionnaire—one that depends on memory, mood in the moment, and a person’s willingness to answer honestly. We explore how digital phenotyping could change that by using signals from smartphones and wearable devices to better understand changes in mood, behavior, and daily functioning over time. From step counts and sleep patterns to broader activity trends, these passive data streams may offer clinicians a more continuous view of mental health. But the promise comes with real-world challenges: device access, syncing problems, missing data, and the risk of widening gaps for people who are already underserved. We also break down the AI methods behind the research in plain language, including why depression scores often contain many zeros, how hurdle models help account for that pattern, why PCA can reduce overfitting, and how Bayesian multi-level modeling fits the messy reality of longitudinal mental health care. The result is a thoughtful look at where digital tools can support depression monitoring, especially for older adults who may face stigma or underreport symptoms, and what needs to happen before these systems can responsibly become part of clinical practice. References: Using Digital Phenotyping for Depression Screening in Community-Dwelling Older Adults: Bayesian Multilevel Hurdle Model Machine Learning Approach Chung 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/

  8. May 28

    #46 - We Expect Patients To Learn Fast When They Feel Worst

    What happens after a scary ER visit when you’re sent home with more paperwork than clarity? For many patients, discharge instructions are dense, stressful, and hard to process—not because they aren’t trying, but because medical information is often delivered at the exact moment fear, fatigue, and overload make learning nearly impossible. We explore why patient education so often falls short: rushed conversations, confusing medical jargon, handouts written above common reading levels, language barriers, and the reality that the most important questions usually come later, once you’re home and finally able to think clearly. Then we turn to a promising AI use case: a voice-activated chatbot designed to help patients understand wet age-related macular degeneration and intravitreal injections, a treatment that can prevent vision loss and even improve sight for some people. The study suggests patients found the chatbot easy to use and understandable, but we ask the bigger question: is a tool that people like enough to improve follow-up, adherence, and outcomes? From there, we dig into what real learning actually requires. Human clinicians don’t just answer questions—they recognize confusion, explain the bigger picture, and move fluidly between education and logistics. That kind of back-and-forth, known as mixed-initiative dialogue, is a crucial design goal for conversational AI, especially voice assistants where timing, interruptions, and tone can shape trust. If you care about health literacy, patient engagement, and safe AI in medicine, this conversation will change how you think about chatbots in healthcare. References: Generative Artificial Intelligence–Driven Voice Assistance for Patient Education in Ophthalmology Jacobs et al. JAMA Eye on 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/

See All (53)

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
    53
  • Rating
    Clean
  • Copyright
    © 2026 Code & Cure
  • Show Website
    Code & Cure

You Might Also Like