AI in Healthcare

TL;DR DSM5AgentFlow uses three cooperating LLM agents—Therapist, Client, and Diagnostician—to simulate DSM‑5 Level‑1 screenings and then generate step‑by‑step diagnoses tied to specific DSM criteria. Experiments across four LLMs show a familiar trade‑off: dialogue‑oriented models sounded more natural, while a reasoning‑oriented model scored higher on diagnostic accuracy. For founders and PMs in digital mental health, the win is auditability: every symptom claim can be traced to a quoted utterance and an explicit DSM clause. The catch: results are built on synthetic dialogues, so ecological validity and real‑world safety remain open. ...

The AI hype in pharma has mostly yielded faster failures. Despite generative models for molecules and AlphaFold for protein folding, the fundamental chasm remains: what works in silico or in vitro still too often flops in vivo. A new proposal — Programmable Virtual Humans (PVHs) — may finally aim high enough: modeling the entire cascade of drug action across human biology, not just optimizing isolated steps. 🧬 The Translational Gap Isn’t Just a Data Problem Most AI models in drug discovery focus on digitizing existing methods. Target-based models optimize binding affinity; phenotype-based approaches predict morphology changes in cell lines. But both ignore the reality that molecular behavior in humans is emergent — shaped by multiscale interactions between genes, proteins, tissues, and organs. ...