Active Evidence-Seeking and Diagnostic Reasoning in Large Language Models for Clinical Decision Support

• URL: http://arxiv.org/abs/2605.22047v1
• Date: Thu, 21 May 2026 06:34:50 GMT
• Title: Active Evidence-Seeking and Diagnostic Reasoning in Large Language Models for Clinical Decision Support
• Abstract summary: We introduce an OSCE-inspired standardized patient simulator and a controlled, reproducible benchmark for active diagnostic inquiry.<n>Across 468 cases and 15 models in our protocol, we observe that multi-turn evidence seeking reduces diagnostic accuracy by 12.75% and lowers supporting-evidence quality by 24.36%.
• Score: 20.0177310948999
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models perform well on static medical examinations, yet clinical diagnosis often requires iterative evidence gathering under uncertainty. Building on prior interactive evaluation efforts, we introduce an OSCE-inspired standardized patient simulator and a controlled, reproducible benchmark for active diagnostic inquiry. Across 468 cases and 15 models in our protocol, we observe that multi-turn evidence seeking reduces diagnostic accuracy by 12.75% and lowers supporting-evidence quality by 24.36% relative to full-context evaluation; error analyses associate these drops with premature diagnostic closure and inefficient questioning. Together, these results suggest that static full-context benchmarks may overestimate performance in interactive evidence-seeking settings, motivating complementary interactive assessment for safer clinical decision support.

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