#45 - How Machine Learning Improves Stroke Prediction With AFib

#45 - How Machine Learning Improves Stroke Prediction With AFib

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 strokes, but they also increase the risk of serious bleeding, making the “right” answer highly dependent on each patient’s risk, context, and values. We begin by breaking down the clinical basics: what AFib is, why clots can form in the atria, and how those clots can lead to stroke. From there, we unpack CHA₂DS₂-VASc, the standard scoring tool used to estimate stroke risk. Its simplicity makes it practical and easy to communicate, but that same simplicity can also be a limitation. Fixed point values do not always capture the complex ways age, medical conditions, medications, and real-world patient factors interact. Then we turn to a paper asking a practical question: can machine learning better predict one-year stroke risk after new-onset AFib using information clinicians usually have available from the start? We explore feature selection with BIC, the importance of external validation, and why even a straightforward logistic regression model can outperform a classic clinical score. We also discuss why XGBoost performs so well with tabular clinical data, how it captures nonlinear thresholds and interactions, and how SHAP explanations can make predictions more transparent and clinically useful. We close with a clear stance on “AI said so” medicine: targeted, interpretable models may help with high-stakes risk prediction, but black-box LLMs are not the right tool for deciding who should receive anticoagulation. References: Interpretable machine learning models for stroke risk prediction in patients with newly diagnosed atrial fibrillation [https://www.nature.com/articles/s41746-026-02470-3] Lin et al. Nature 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/

#44 - AI For Dementia Care

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 explore how AI could support dementia care in practical, meaningful ways, while also asking where the technology could cause harm if it is designed without empathy, usability, and real-world caregiving constraints in mind. We break down what dementia is—and what it isn’t—across Alzheimer’s disease, vascular dementia, Lewy body dementia, and frontotemporal dementia. Because symptoms and progression vary so widely, assistive technology has to adapt over time, often becoming simpler as a person’s needs change. From there, we look at early detection tools that use machine learning to analyze speech, facial expressions, gait, typing patterns, and everyday behaviors to identify risk earlier and guide screening. The conversation also moves into daily life: smart pill dispensers, reminders for meals and hygiene, home monitoring, wearables, fall prediction, and wandering alerts. We also examine cognitive support tools like reminiscence therapy, where personalized photos, music, and life stories can help strengthen mood, memory, and connection through conversational AI and voice-based interfaces. But the promise of AI comes with difficult questions. How do we avoid overwhelming caregivers with constant alerts? When does safety monitoring become surveillance? And what happens when social chatbots reduce loneliness while creating one-sided emotional bonds? For anyone interested in dementia support, caregiver burnout, digital health, and the future of eldercare, this episode offers a practical map of where AI is already showing promise—and why thoughtful, human-centered design matters just as much as the technology itself. References: Assistive Intelligence: A Framework for AI-Powered Technologies Across the Dementia Continuum [http://mdpi.com/2673-9259/6/1/8] Mohapatra et al. Journal of Ageing and Longevity (2026) Introduction to Large Language Models (LLMs) for dementia care and research [https://www.frontiersin.org/journals/dementia/articles/10.3389/frdem.2024.1385303/full] Treder et al. Frontiers in Dementia (2024) Demo: Can Visual Stimulation Enhance Reminiscence-Therapy Chatbot? [https://openreview.net/pdf?id=Bv1yogTK2q] Kononovych et al. NeurIPS Workshop GenAI for Health (2025) Exploring the Design of Generative AI in Supporting Music-based Reminiscence for Older Adults [https://dl.acm.org/doi/10.1145/3613904.3642800] Jin et al. CHI Conference on Human Factors in Computing Systems (2024) Credits: Theme music: Nowhere Land, Kevin MacLeod (incompetech.com) Licensed under Creative Commons: By Attribution 4.0 https://creativecommons.org/licenses/by/4.0/

#43- AI Hype Vs Real-World Medicine

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 decision-making. We unpack how today’s AI excitement fits into a much longer history of bold promises, from the early optimism of the Dartmouth Conference to modern “AI summers” driven by funding, media attention, and novelty. They also explore what an “AI winter” really means, why confidence can collapse quickly, and how today’s ecosystem makes exaggeration easier to spread and harder to correct. Then we turn to the realities of emergency care. ER triage is not about guessing one diagnosis or producing a neat top-five list. It is about urgency, risk, and judgment under uncertainty: identifying life-threatening possibilities, deciding what tests come next, and determining who needs immediate care, admission, or safe discharge. The conversation also highlights a major limitation of text-only AI evaluations: medical charts are already shaped by human clinicians, meaning the model may be relying on information that required real-world expertise to gather in the first place. For anyone interested in trustworthy AI in healthcare, medical diagnosis, health misinformation, and the responsible use of large language models in clinical settings, this episode offers a clearer way to think beyond the headline. References: Performance of a large language model on the reasoning tasks of a physician [https://pubmed.ncbi.nlm.nih.gov/42060751/] Brodeur et al. Science (2026) Did AI really beat ER doctors at ER triage? Nope. A look at an interesting AI study that has led to some very overhyped headlines. [https://youcanknowthings.substack.com/p/did-ai-really-beat-er-doctors-at] Kristen Panthagani You can know Things, Substack (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/

#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 [https://jamanetwork.com/journals/jamapsychiatry/article-abstract/2846835] 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/

#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 [https://www.medrxiv.org/content/10.64898/2026.02.24.26347028v1] Gibson et al. medRxiv Preprint (2026) Dozens of AI disease-prediction models were trained on dubious data [https://www.nature.com/articles/d41586-026-00697-4#ref-CR1] 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/