From Hindsight to Foresight: Dissecting Clinical Evidence Acquisition in Large Language Models
Large language models (LLMs) often diagnose full sight, static clinical cases well yet struggle in foresight, dynamic diagnosis. Prior work has improved this limitation by quantifying the information models acquire and by developing stronger follow-up strategies to improve diagnostic accuracy. In contrast, we introduce a distinctive hindsight to systematically dissect why LLMs fail to acquire diagnostically critical evidence, tracing the failure from evidence identification, utilization and downstream diagnostic updating through not only diagnostic accuracy, but also evidence coverage, trajectory dynamics, and elicited probability and information entropy. Starting experiments from the bre...
| Authors | Ziyang Ling |
|---|---|
| Primary category | cs.CL |
| Published | 13 October 2026 |
| Last updated at source | 13 October 2026 |
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