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/