Code & Cure

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&ap...

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 f...

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...

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 abrup...

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...

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...

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: rushe...

What if an irregular heartbeat could quietly set the stage for a stroke? Atrial fibrillation is common, often confusing, and potentially dangerous because it can allow blood to pool in the heart, form clots, and send them traveling to the brain. The challenge is not simply knowing that AFib raises stroke risk—it is deciding who truly needs anticoagulation. Blood thinners can prevent devastating st...

What if artificial intelligence could help make dementia care feel less like a 36-hour day? Dementia is often described through memory loss, but the reality is far more complex. For caregivers, the hardest part may be the constant vigilance: tracking medications, preventing falls, managing wandering, responding to changing behaviors, and trying to preserve dignity and connection along the way. We...

What if the headline “AI outperformed doctors” is asking the wrong question? When a Harvard emergency triage study makes waves, it’s easy to focus on the most dramatic takeaway. But the real story is more complicated: what did the study actually test, and what parts of emergency medicine did it leave out? We slow down the hype and take a closer look at what AI can and cannot tell us about clinical...

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....

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 essentia...

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 oph...

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 d...

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...

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...

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...

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 persp...

What if an AI health chatbot told you to stay home when you actually needed emergency care? In this episode, we put ChatGPT Health under the microscope using a clinician-authored evaluation designed to test a critical question: can an AI safely guide people on whether to go to the ER, visit urgent care, or wait it out at home? The results reveal a troubling pattern. When symptoms fall into the “mi...

What if the most confident answer in the room is also the most misleading? Large language models can ace medical exams, yet falter when faced with a real person’s messy, incomplete story. In this episode, we explore how that gap plays out in one of medicine’s highest-stakes decisions: triage. Drawing on Laura’s experience in emergency medicine and Vasanth’s background in AI research, we unpack a n...

What if the most reliable ways to track fertility are also the simplest? In this episode, we examine the science of ovulation timing and hold modern wearables to a high standard, comparing passive temperature and vital sign data with established methods like LH surge testing and cervical mucus observation. Drawing on perspectives from a cognitive scientist and an emergency physician, we explain wh...

Fluent summaries that cannot prove their claims are a hidden liability in healthcare, quietly eroding clinician trust and wasting time. In this episode, we walk through a practical system that replaces “sounds right” narratives with evidence-backed summaries by pairing retrieval augmented generation with a large language model that serves as a judge. Instead of asking one AI to write and police it...

What if the earliest warning sign of an opioid overdose surge isn’t locked inside a delayed report, but unfolding in real time on Reddit? In this episode, we explore how social media conversations, especially pseudonymous, community-led forums, can reveal emerging overdose risks before traditional surveillance systems catch up. We unpack research that analyzed more than a decade of posts to show h...

What really happens when a “smart” system steps into the operating room, and collides with the messy, time-pressured reality of clinical care? In this episode, we unpack a multi-center pilot that streamed audio and video from live surgeries to fuel safety checklists, flag cases for review, and promise rapid, actionable insight. What emerged instead was a clear-eyed lesson in the gap between aspira...

Can you trust a confident answer, especially when your health is on the line? This episode explores the uneasy relationship between language fluency and medical truth in the age of large language models (LLMs). New research asks these models to rate their own certainty, but the results reveal a troubling mismatch: high confidence doesn’t always mean high accuracy, and in some cases, the least reli...