Large language models exhibit unreliable updating of clinical judgment as patient evidence evolves

Abstract:Large language models (LLMs) are increasingly explored for clinical reasoning, but whether they appropriately revise judgments as patient evidence evolves remains unclear. We evaluated longitudinal belief updating using matched intensive-care trajectories from electronic health records. Across diverse LLMs, conditioning on a preceding judgment more often increased than reduced prediction error when estimates changed, replicated for a second endpoint. Controlled interventions revealed two failure modes. First, with preceding assessment fixed, models responded more strongly to worsening than matched improving respiratory evidence; this asymmetry persisted after headroom normalization at moderate and strong evidence levels. Second, with current evidence fixed, increasing prior risk from 10% to 90% shifted estimates by 26.2 percentage points, demonstrating causal influence of prior model beliefs. Prompting did not restore reliable updating. Evidence-Validated Longitudinal Update (EVLU) identified fewer, more reliable revisions, revealing a reliability-coverage trade-off. These findings establish longitudinal belief updating as a distinct dimension of LLM reliability.

Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

arXiv:2610.02684 [cs.AI]

(or arXiv:2610.02684v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.02684 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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