Implications of integrating large language models into clinical decision making

Abstract

Christof & Armoundas explore how large language models (LLMs) can augment clinician-level clinical reasoning across the three pillars-framing the encounter, diagnostic reasoning, and treatment/management-highlighting gains in information synthesis and pattern recognition while underscoring limits that require continuous human judgment and oversight. They advocate a bias-aware, privacy-preserving, and rigorously validated “human-in-the-loop” deployment that safeguards patient agency and clinical accountability, while integrating LLMs into real-world workflows via clear clinician imperatives.

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