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 guidelines?

In this episode, we explore how retrieval-augmented generation (RAG) is reshaping medical coding. Laura, an emergency physician, shares what it’s really like to code in the middle of clinical chaos. Vasan, an AI engineer, explains why standard large language models often hallucinate ICD-10 and CPT codes—and how RAG brings the conversation back to solid ground with verifiable sources, official codebooks, and audit-ready citations.

We unpack a recent study comparing clinician-assigned codes to RAG-augmented outputs on actual emergency department charts. The results? When reviewers didn’t know which was which, they often chose the AI-generated codes—ones that captured true clinical meaning, like “alcoholic gastritis without bleeding” instead of the vague “epigastric pain.”

Beyond accuracy, we dive into the ripple effects: cleaner claims, fewer denials, stronger datasets for research—and the essential guardrails that keep things safe and ethical, from privacy safeguards to human review and confidence scoring.

If documentation has ever pulled you away from patient care, this episode offers a hopeful shift. Learn where retrieval-based coding tools fit into your EHR workflow, how clinicians can stay in the loop, and which high-volume complaints to tackle first for maximum impact.

Subscribe for more deep dives into clinician-centered AI, share this with the colleague who always codes “unspecified,” and leave us your biggest documentation headache—we’ll decode it next.

Reference:

Assessing Retrieval-Augmented Large Language Models for Medical Coding
Eyal Klang et al.
New England Journal of Medicine (NEJM) AI, 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/

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 ahead.

We start with familiar ground—cardiovascular risk scores—and explore how predictions only matter when they guide meaningful actions: improved blood pressure control, appropriate statin use, and lifestyle changes that truly bend the curve. But Delphi doesn’t stop at single conditions. It captures the real-world complexity of multimorbidity, mapping how diseases co-occur and unfold over time.

Delphi doesn’t “understand” biology—it recognizes patterns. Much like a weather forecast, it turns complex statistical relationships into calibrated probabilities. We break down how the model handles irregular patient histories, simultaneous diagnoses, and time-to-event forecasting—offering practical insights clinicians can use. We also explore how Delphi was validated across extensive UK and Danish datasets, and why “reliable” beats “flashy” in the real world of medicine.

One of Delphi’s most promising features? Generative timelines. By simulating possible health futures from partial records, the model creates synthetic patients—fueling research while protecting privacy.

At the core is a human question: would you want to know your likely diagnoses decades in advance? We unpack the emotional and ethical dimensions of predictive health—when foresight helps, when it overwhelms, and how to responsibly deliver these insights. If you care about AI in healthcare, predictive analytics, or the ethics of foreknowledge, this episode offers a grounded look at what’s here, what’s coming, and how to use it wisely.

Reference:

Learning the natural history of human disease with generative transformers
Artem Shmatko et al.
Nature, 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/

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 presents with tremors, muscle rigidity, and slowed movement. But it also affects facial muscles, leading to “hypomimia”—a loss of expressiveness where smiles become slower, less intense, and less spontaneous. Using the Facial Action Coding System, researchers broke down these expressions into measurable muscle movements like the “lip corner puller” and “dimpler,” allowing AI to analyze them with clinical precision.

Interestingly, models trained specifically on smile-related features outperformed those using broader facial data, showing that a targeted approach may yield better diagnostic results. This innovation blends expert medical knowledge with AI—not as a mysterious black box, but as a transparent and focused tool for real-world screening.

While promising, the technology isn’t without challenges. False positives and issues with lighting, camera quality, and cultural differences in facial expressions highlight the need for more testing before widespread use. Still, in clinical settings, especially where neurologists are scarce, this tool could offer meaningful support.

Tune in to explore how artificial intelligence is helping decode the smallest of human expressions—and what that might mean for the future of neurological care.

References:

AI‑Enabled Parkinson’s Disease Screening Using Smile Videos
T. Adnan, et al.
NEJM AI, 2025

Automated video-based assessment of facial bradykinesia in de-novo Parkinson’s disease
Michal Novotny et al.
npj, Nature Digital Medicine, 2022

Detection of hypomimia in patients with Parkinson’s disease via smile videos
G. Su, et al.
Annals of Translational Medicine, 2021

Analysis of facial expressions in parkinson's disease through video-based automatic methods
Andrea Bandini et al
Journal of Neuroscience Methods, 2017

Credits:

Theme music: Nowhere Land, Kevin MacLeod (incompetech.com)
Licensed under Creative Commons: By Attribution 4.0
https://creativecommons.org/licenses/by/4.0/

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 was removed.

This episode unpacks how AI isn’t just a helpful assistant—it may be reshaping how physicians think, reason, and make decisions. Unlike a stethoscope or scalpel, which extends physical capabilities, AI intervenes in cognitive processes. What happens when that crutch is suddenly gone?

We delve into the subtle but important distinctions between tools that amplify skill and those that risk replacing it. From seasoned practitioners to medical trainees raised on AI support, we ask: what kind of clinician emerges when core diagnostic thinking is offloaded to machines?

Through the lens of interaction design, we explore different models for integrating AI—whether as a second reader, background assistant, or tightly scoped tool—and how each impacts long-term expertise. The right design, we argue, could support true human-AI partnerships without compromising clinical judgment.

Tune in for a provocative conversation that challenges simplistic narratives about technology in healthcare—and rethinks what it means to be an expert in the age of artificial intelligence.

References

Are A.I. Tools Making Doctors Worse at Their Jobs?
Teddy Rosenbluth
The New York Times, August 28, 2025

Endoscopist deskilling risk after exposure to artificial intelligence in colonoscopy: a multicentre, observational study
Krzysztof Budzyń et al.
The Lancet Gastroenterology & Hepatology, 2025

Relying on AI in Colonoscopies May Erode Clinicians' Skills
Joedy McCreary
MedPage Today, August 12, 2025

Expert reaction to observational study looking at detection rate of precancerous growths in colonoscopies by health professionals who perform them before and after the routine introduction of AI
Science Media Centre, August 12, 2025

Upskilling or deskilling? Measurable role of
an AI-supported training for radiology
residents: a lesson from the pandemic
Mattia Savardi et al.
Insights into Imaging, European Society of Radiology, 2025

AI-induced Deskilling in Medicine: A Mixed-Method Review
and Research Agenda for Healthcare and Beyond
Chiara Natali et al.
Artificial Intelligence Review, 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/

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 technology (ADT)—AI tools that automatically generate clinical notes by listening to patient-doctor conversations. On paper, the promise is bold: let physicians focus on care, not charting. But reality is more complicated.

Laura shares her firsthand experience with late-night charting and the emotional toll of juggling empathy and efficiency. We unpack the deeper roots of burnout—beyond paperwork—including overwhelming patient loads, chronic understaffing, and a culture that often punishes vulnerability.

AI-generated notes surface an intriguing paradox: human communication is effortless for doctors, but incredibly complex for machines. What a physician instantly grasps from a patient’s gesture or tone can easily confuse an AI system. The result? Notes that sometimes omit critical context, add irrelevant details, or introduce factual errors.

Early research reveals mixed outcomes—some clinicians spend extra hours editing AI notes, defeating the intended time savings. Yet there’s potential. With advances in multimodal input and smarter evaluation tools, ADT could still become a powerful support tool—not to replace doctors, but to restore their time.

Tune in to discover why turning conversation into clinical documentation is one of AI’s most challenging—and potentially transformative—tasks in modern healthcare.

References:

Evaluation of an Ambient Artificial Intelligence Documentation Platform for Clinicians
Stults CD, McDonald KM, Niehaus KE, et al.
JAMA Network Open, 2025

Ambient Artificial Intelligence Scribes to Alleviate the Burden of Clinical Documentation
Tierney AA, Gayre G, Hoberman B, et al.
NEJM Catalyst Innovations in Care Delivery, 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/

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 technologies are transforming blood pressure tracking. From smartwatches and rings to toilet seats and even facial recognition, wearable devices are pushing the boundaries of what's possible—no cuffs required. You’ll learn how sensors using light (PPG), electrical signals, and video can estimate blood pressure in real time, offering the promise of continuous, hassle-free monitoring.

But as with any innovation, there are hurdles. We dive into critical challenges like calibration complexity, variable accuracy across users and activities, and whether these tools truly improve hypertension management or simply add more data noise. The role of artificial intelligence adds another layer—enhancing insights, but also raising new questions about equity, access, and interpretation.

Is convenience enough to spark a shift in how we manage cardiovascular health? Or do these tools need to prove more than novelty to become essential?

Tune in for a forward-looking conversation on the promise, the pitfalls, and the future of blood pressure technology—where innovation meets one of medicine’s most familiar numbers.

References:

Emerging sensing and modeling technologies for wearable and cuffless blood pressure monitoring
Lei Zhao, Cunman Liang, Yan Huang, Guodong Zhou, Yiqun Xiao, Nan Ji, Yuan‑Ting Zhang, Ni Zhao et al.
Nature, Digital Medicine, May 2023

Cuffless Blood Pressure Measurement Devices – International Perspectives on Accuracy and Clinical Use: A Narrative Review
Eugene Yang, Aletta E. Schutte, George Stergiou, Fernando Stuardo Wyss, Yvonne Commodore‑Mensah, Augustine Odili, Ian Kronish, Hae‑Young Lee, Daichi Shimbo
JAMA Cardiology, June 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/

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 missed appointments. The top signal? How far in advance the appointment was booked. Appointments scheduled more than 60 days out had the highest odds of being missed—more telling than age, income, or insurance status. Other key factors included continuity with the same provider, a patient’s past attendance, distance to the clinic, and even the weather.

This episode unpacks how models like random forests and gradient boosting sift through massive datasets to identify no-show risks—not just for populations, but for individual patients. These insights open the door to smarter, more personalized interventions: tighter scheduling windows, transportation support, or ensuring patients see familiar faces.

Tune in to explore how AI could help healthcare systems run smoother, deliver more timely care, and keep more patients from vanishing in the first place.

References:

Predicting Missed Appointments in Primary Care: A Personalized Machine Learning Approach
Wen-Jan Tuan, Yifang Yan, Bilal Abou Al Ardat, Todd Felix and Qiushi Chen
Annals of Family Medicine, July/August 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/

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-based helper, offering pre-vetted encouragement and resources. But after an AI upgrade, users began receiving rigid diet tips: restrict calories, aim for weekly weight loss goals, and obsessively track measurements—precisely the advice no one battling an eating disorder should hear. What should have been a lifeline revealed the danger of unguarded algorithmic “help.”

We trace this journey from the earliest chatbots—think ELIZA’s therapeutic mimicry in the 1960s—to today’s sophisticated large language models. Along the way, we highlight why shifting from scripted responses to free-form generation opens doors for innovation in healthcare and, simultaneously, for unintended harm. Crafting effective guardrails isn’t just a technical challenge; it’s a moral imperative when lives hang in the balance.

As providers eye AI to extend care, Tessa’s story offers vital lessons on rigorous testing, transparency around updates, and the irreplaceable role of human oversight. Despite the pitfalls, we close on a hopeful note: with the right safeguards, AI can amplify human expertise—transforming support for vulnerable patients without losing the empathy and nuance only people can provide.

Reference:

National Eating Disorders Association phases out human helpline, pivots to chatbot
Kate Wells
NPR, May 2023

An eating disorders chatbot offered dieting advice, raising fears about AI in health
Kate Wells
NPR, June 2023

The Unexpected Harms of Artificial Intelligence in Healthcare
Kerstin Denecke Guillermo Lopez-Compos, Octavio Rivera-Romero, and Elia Gabarron
Studies in Health Technology and Informatics, May 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/