arXiv AI By Dipayan Sengupta, Saumya Panda

How Clinicians Think and What AI Can Learn From It

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arXiv AI
Aug 18

Generated Context versus Governed State: Functional Conditions for Accountable Longitudinal Clinical Reasoning

arXiv:2608. 14804v1 Announce Type: new Abstract: Large language models (LLMs) have become the dominant interface of clinical artificial intelligence, yet the interface they expose (text in, text out, one context window at a time) maintains no explicit, persistent, governed representation of what is currently true about a patient.

By Augusto Bernardo Pissarra, Victor Lorena de Farias Souza
arXiv AI
Sep 2

AI Morbidity and Mortality: A Framework for Clinical AI Failure Review

AI Morbidity and Mortality (AI M&M) is a structured, blameless framework designed to review clinical AI failures. It combines standardized case intake, evidence preservation, investigator reconstruction, tool‑in‑loop attribution, and corrective‑action tracking, classifying each event across four linked dimensions: Trigger, Mechanism, Clinical Pathway, and Corrective Action. The authors demonstrate the framework with five outpatient medication and clinical decision‑support cases, achieving full agreement among reviewers on all classification axes.

By Paulius Mui, Dean F. Sittig, Steve Labkoff, Sanjay Basu