Code & Cure

What if one night in a sleep lab could offer a glimpse into your long-term health? Researchers are now using a foundation model trained on hundreds of thousands of hours of sleep data to do just that, by predicting the next five seconds of a polysomnogram, the model learns the rhythms of sleep and, with minimal fine-tuning, begins estimating risks for conditions like Parkinson’s, dementia, heart f...

What if your keyboard could reveal your mental health? Emerging research suggests that how you type—not what you type—could signal early signs of depression. By analyzing keystroke patterns like speed, timing, pauses, and autocorrect use, researchers are exploring digital biomarkers that might quietly reflect changes in mood. In this episode, we break down how this passive tracking compares to tra...

What happens when a chatbot follows the wrong voice in the room? In this episode, we explore the hidden vulnerabilities of prompt injection, where malicious instructions and fake signals can mislead even the most advanced AI into offering harmful medical advice. We unpack a recent study that simulated real patient conversations, subtly injecting cues that steered the AI to make dangerous recommend...

What if a routine mammogram could do more than screen for breast cancer? What if that same image could quietly reveal a woman’s future risk of heart disease—without extra tests, appointments, or burden on patients? In this episode, we explore a large-scale study that uses deep learning to uncover cardiovascular risk hidden inside standard breast imaging. By analyzing mammograms that millions of wo...

What if the future of antivenom didn’t come from horse serum, but from AI models that shape lifesaving proteins out of noise? In this episode, we explore how diffusion models, powerful tools from the world of AI, are transforming the design of antivenoms, particularly for some of nature’s deadliest neurotoxins. Traditional antivenom is costly, unstable, and can provoke serious immune reactions. Bu...

What if the words we use could tip the balance between seeking help and staying silent? In this episode, we explore a fascinating study that compares top-voted Reddit responses with replies generated by large language models (LLMs) to uncover which better reduces stigma around opioid use disorder—and why that distinction matters. Drawing from Laura’s on-the-ground ER experience and Vasanth’s resea...

How can AI-powered care truly serve rural communities? It’s not just about the latest tech, it’s about what works in places where internet can drop, distances are long, and people often underplay symptoms to avoid making a fuss. In this episode, we explore what it takes for AI in healthcare to earn trust and deliver real value beyond city limits. From wearables that miss the mark on weak broadband...

What if your discharge instructions were written in a language you couldn’t read? For millions of patients, that’s not a hypothetical, but a safety risk. And at 2 a.m. in a busy hospital, translation isn’t just a convenience; it’s clinical care. In this episode, we explore how AI can bridge the language gap in discharge instructions: what it does well, where it stumbles, and how to build workflows...

What if your health data spoke in one calm voice instead of twenty buzzing ones? In this episode, we explore an AI “interpreter layer” that turns step counts, sleep stages, and alerts into fewer, smarter signals that nudge real behavior—without the anxiety spiral. Vasanth (AI researcher and cognitive scientist) and Laura (emergency physician) bring lab insight and frontline reality to a problem mo...

What happens when your health chatbot sounds helpful—but gets the facts wrong? In this episode, we explore how AI systems, especially large language models, can prioritize pleasing responses over truthful ones. Using the common confusion between Tylenol and acetaminophen, we reveal how a friendly tone can hide logical missteps and mislead users. We unpack how these models are trained—from next-tok...

What if digital assistants could triage symptoms, schedule appointments, and coordinate rides—all while doctors focus on the human side of care? That’s the promise of multi-agent AI in healthcare. In this episode, we explore how these intelligent teams of agents are transforming both clinical and operational workflows. We begin by breaking down what an AI “agent” really is: not just a chatbot, but...

Artificial intelligence promises faster notes, smoother workflows, and smarter clinical decisions. But behind every seamless interaction lies an invisible cost—electricity, water, and carbon emissions that rarely enter the healthcare conversation. In this episode, we trace what happens after you hit “enter” on a clinical prompt. From power-hungry GPUs to evaporative cooling systems in data centers...

What if the person who knows you best isn’t the best person to speak for you when it matters most? We explore a study that tested just that—comparing the CPR preferences predicted by loved ones with those predicted by machine learning. The result? Algorithms got it right more often. That surprising outcome raises tough, important questions: Why do partners misjudge? And could AI really support lif...

What happens when an algorithm—not a doctor or a claims reviewer—denies your surgery? A single decision like that can trigger a much bigger conversation about how AI is reshaping access to care. In this episode, we dive into Medicare’s WISER pilot and the complex world of prior authorization. What’s the goal? Reduce waste and streamline approvals. But where does it go wrong—and how can we fix it?...

What if AI could turn chaotic clinical notes into clean, billable codes—without sacrificing accuracy or trust? Every shift, emergency physicians face the same grind: time-crunched documentation, symptom-first note-taking, and the constant lure of the “unspecified” box just to move on. But what if a system could read between the lines—and suggest precise, payer-accepted codes grounded in real guide...

What if we could glimpse our future health—not through guesswork, but through data-driven forecasts? A new AI model, codenamed “Delphi,” is redefining what it means to predict disease by learning from massive, population-scale medical histories. Built on transformer architecture, Delphi estimates the risk and timing of over a thousand possible diagnoses—offering a personalized view of what may lie...

Can a smile reveal the early signs of Parkinson’s disease? New research suggests it can—and AI is making that detection possible. Scientists are training machine learning systems to spot subtle facial changes associated with Parkinson’s, particularly in how we smile. These early signs, often missed by the human eye, could hold the key to faster, more accessible diagnosis. Parkinson’s typically pre...

Could AI be making doctors worse at their jobs? As artificial intelligence becomes a trusted tool in modern medicine, a surprising question emerges: could relying on these systems actually erode human expertise? We explore a compelling study from The Lancet that found a 6% drop in detection rates for endoscopists who initially used AI to identify precancerous polyps—then lost that edge once the AI...

AI is writing medical notes, but can doctors trust what it creates? Burnout is quietly eroding the medical workforce—and documentation overload is a major culprit. Physicians now spend nearly half their workday writing notes instead of treating patients, pushing many to the brink of exhaustion. Could artificial intelligence offer a lifeline? In this episode, we explore ambient documentation techno...

What if checking your blood pressure was as easy as glancing at your watch? High blood pressure quietly affects nearly half of all Americans—yet it's one of the most preventable causes of strokes, heart attacks, and other serious health problems. The catch? Traditional monitoring methods are clunky, inconvenient, and rarely used outside the clinic. In this episode, we explore how next-gen tec...

Why do so many doctor’s appointments end in empty waiting rooms? Nearly one in four scheduled visits turn into no-shows, disrupting care, wasting resources, and straining already overburdened systems. But a new study shows we might be able to see these gaps coming—and stop them. By analyzing over a million healthcare visits, researchers used machine learning to uncover surprising predictors of mis...

What if a chatbot designed to support recovery instead encouraged the very behaviors it was meant to prevent? In this episode, we unravel the cautionary saga of Tessa, a digital companion built by the National Eating Disorder Association to scale mental health support during the COVID-19 surge—only to take a troubling turn when powered by generative AI. At first, Tessa was a straightforward rules-...

What if an AI could write your medical chart—and what happens when it gets it wrong? Doctors have long lamented the paperwork that comes with every patient encounter. “Charting was the bane of my existence,” admits Dr. Laura Hagopian, an emergency physician who’s spent countless hours piecing together fragmented notes and outdated records. Could artificial intelligence finally lift this administra...

What if a 19th-century nurse laid the foundation for 21st-century disease surveillance? Florence Nightingale, widely known for her compassion, was also a pioneering statistician who used data to reveal a hidden crisis: more soldiers in the Crimean War were dying from infections than from battle wounds. Her insights led to life-saving reforms—and sparked a revolution in how we understand public hea...

What if the next breakthrough treatment for a rare disease was already sitting on the pharmacy shelf? Drug repurposing, the science of finding new uses for existing medications, is transforming how we discover treatments, blending serendipity with strategy. It began with surprises like Viagra, a heart drug turned blockbuster, but today it's driven by advanced data tools that accelerate discov...