Cognition, oversight, and the safety case for documentation AI.

Authors

Fahad Wali Ahmed, Fauwad Ahmed

Published in

Journal of hospital medicine. Oct 15, 2026. Epub Oct 15, 2026.

Abstract

Large language models (LLMs) can now draft hospital discharge summaries. Reported hallucination rates vary by measurement context, from about 1%-2% in structured summarisation to over 60% under provocation testing. The common argument that LLMs outperform a tired resident treats a high-stakes clinical and legal document as a text-quality problem. We argue that a potentially underappreciated concern is the weakening of clinician oversight when reviewing fluent machine-generated text, and that the joint clinician-AI system is central to safety. We propose a Clinician-AI Oversight Model with four safeguards: bounded autonomy, structured verification, error-signal monitoring, and longitudinal evaluation of cognitive effects.

PMID:
42878157
Bibliographic data and abstract were imported from PubMed on 16 Oct 2026.

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