Active Evidence-Seeking and Diagnostic Reasoning in Large Language Models for Clinical Decision Support
Chen Zhan, Xihe Qiu, Xiaoyu Tan, Xibing Zhuang, Gengchen Ma, Yue Zhang, Shuo Li, Peifeng Liu
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.
AI Impact Assessments
(1 model)Scientific Impact Assessment: Active Evidence-Seeking and Diagnostic Reasoning in LLMs for Clinical Decision Support
1. Core Contribution
This paper introduces ROUNDS-Bench, an OSCE-inspired interactive evaluation framework that benchmarks LLMs on their ability to actively seek clinical evidence through multi-turn dialogue rather than passively diagnose from complete case information. The central finding is a consistent "capability gap": across 468 cases and 15 models, transitioning from full-context diagnosis (Task 1) to active evidence-seeking (Task 2) reduces diagnostic accuracy by 12.75% and evidence quality by 24.36% on average. The paper also introduces the concept of "hallucinated reasoning"—correct diagnoses produced without adequate evidentiary grounding—and proposes metrics (StrictEQ, FSA) to detect this failure mode.
The problem addressed is genuinely important: current medical LLM benchmarks overwhelmingly evaluate models under unrealistic full-information conditions, potentially inflating perceived readiness for clinical deployment. The paper makes a convincing case that interactive evidence acquisition is a qualitatively different cognitive task from static pattern matching.
2. Methodological Rigor
Strengths in design:
Methodological concerns:
3. Potential Impact
The paper addresses a real and growing concern in medical AI evaluation. As LLMs are increasingly proposed for clinical decision support, the demonstration that static benchmark performance significantly overestimates interactive capability is valuable for:
However, impact may be limited by the fact that several prior works (MediQ, AMIE, MedQA-CS, MedAgentBench) have already established interactive evaluation as important. The paper positions itself as offering better controllability and reproducibility than these predecessors, but the comparison is largely qualitative rather than demonstrated empirically.
4. Timeliness & Relevance
The paper is highly timely. The rush to deploy LLMs in healthcare—combined with impressive but potentially misleading static benchmark scores—creates an urgent need for more realistic evaluations. The gap between static and interactive performance is a finding the community needs, even if the specific magnitude depends on benchmark design choices. The scaling law analysis and reasoning architecture comparisons are also timely given the rapid proliferation of model families.
5. Strengths & Limitations
Key strengths:
Notable limitations:
Summary
This is a solid benchmarking contribution that quantifies an important and timely phenomenon—the gap between static and interactive diagnostic performance in LLMs. The framework design is sensible though not without significant methodological caveats, particularly the absence of human validation and statistical rigor. The findings are largely expected but usefully quantified, and the evidence quality analysis adds meaningful safety-relevant insights. The work is incremental relative to prior interactive evaluation efforts but offers improved structure and reproducibility.
Generated May 22, 2026
Comparison History (21)
Paper 2 likely has higher scientific impact due to clearer, high-stakes real-world applicability (clinical decision support), a broadly reusable OSCE-style interactive benchmark, and direct implications for safety/regulation by showing static tests overestimate performance. Its methodology appears controlled and reproducible across many cases/models, enabling follow-on work in medicine and interactive evaluation. Paper 1 is timely and novel for AI governance auditing, but is more tightly coupled to specific lab specifications and toolchains, potentially narrowing adoption and cross-field impact despite relevance to alignment and policy.
Paper 1 addresses a critical, high-stakes domain (healthcare) by revealing significant flaws in how medical LLMs are currently evaluated. Demonstrating that static benchmarks overestimate interactive diagnostic performance has profound implications for patient safety and AI deployment in clinical settings. While Paper 2 provides a valuable benchmark for software agents, Paper 1's focus on clinical reasoning under uncertainty directly impacts the safe integration of AI in life-critical applications, giving it a broader and more urgent scientific and societal impact.
Paper 2 addresses a fundamental conceptual gap in LLM research by providing a taxonomy and shared vocabulary for AI sycophancy—a cross-cutting concern affecting alignment, safety, and governance. Its breadth of impact spans multiple fields (AI safety, policy, HCI), and its contributions (taxonomy from 70 papers, survey of 106 experts) offer a foundational framework that will shape future research directions. Paper 1 makes a valuable but narrower contribution to clinical LLM evaluation. While important, its findings (that interactive settings reduce LLM accuracy) are somewhat expected, whereas Paper 2's conceptual clarification has broader, more lasting influence on the field.
Paper 1 addresses a critical safety gap in a high-stakes domain (healthcare) by demonstrating that static benchmarks significantly overestimate LLM performance in realistic, interactive clinical settings. This finding has immediate, profound implications for the deployment of medical AI. While Paper 2 offers a broad, multi-disciplinary methodological benchmark, Paper 1's direct relevance to patient safety and its timely critique of current AI evaluation paradigms give it higher potential for immediate and critical real-world impact.
Paper 2 has higher likely impact because it introduces a controlled, reproducible interactive benchmark (OSCE-style simulator) directly addressing a timely safety-critical gap: LLM performance under active evidence seeking for clinical decision support. It evaluates many models across hundreds of cases with quantitative findings and error analysis, making it immediately useful to the medical AI, evaluation, and safety communities and likely to shape subsequent benchmarking and deployment guidance. Paper 1 is conceptually novel for agent system architecture, but its impact is more speculative and depends on broader adoption and stronger empirical validation.
Paper 2 introduces a principled new metric (Synergistic Faithfulness) grounded in Shapley interaction to isolate true cross-modal contributions, addressing a clear failure mode (evaluation collapse) in VLM explainability. It offers strong methodological rigor (theory-backed metric, high surrogate correlation, large speedup, multi-model/multi-dataset evaluation) and broad relevance across multimodal ML, XAI, safety, and auditing. Paper 1 provides an important benchmark and negative result for clinical LLM interaction, but its impact is narrower (clinical decision support) and more incremental relative to existing interactive evaluation efforts.
Paper 2 is more novel and broadly impactful: it advances autonomous agents from prompt/memory tweaking to verified source-level self-rewriting with a concrete, deployable pipeline (evidence batching, deterministic stages, replay-based verification, safe promotion/rollback). This could generalize across many agent systems and application domains, with clear real-world operational benefits. Paper 1 offers timely, rigorous evaluation for clinical LLMs and important safety insights, but it is primarily a benchmarking/diagnostic analysis contribution with narrower domain impact compared to a general mechanism for self-improving production agents.
Paper 2 has higher likely impact: it introduces a timely, clinically relevant interactive benchmark (OSCE-inspired patient simulator) addressing a known evaluation gap for LLMs (active evidence seeking vs static full-context). The results are broadly applicable across AI safety, NLP evaluation, and medical decision support, and can directly influence model development, deployment practices, and regulatory/clinical assessment. Paper 1 is novel and rigorous within ASP theory, but its applications and cross-field reach are narrower, making its near-term real-world and broad scientific impact likely lower.
Paper 2 has higher potential impact due to its timeliness and direct real-world application to clinical decision support, where safety and evaluation methodology are urgent. Introducing an OSCE-inspired simulator and reproducible interactive benchmark across many models/cases can reshape how medical LLMs are assessed, revealing systematic shortcomings (premature closure, inefficient questioning) that static benchmarks miss. This evaluation framework is likely to influence both clinical AI deployment and broader interactive LLM benchmarking. Paper 1 is innovative and useful for long-horizon agents, but its impact is more engineering-focused and less immediately tied to high-stakes domains.
Paper 2 has higher impact potential due to stronger novelty and broader applicability: a large, guaranteed-solvable, multi-turn, long-horizon benchmark spanning 5 realistic drug-design task types, 102 targets, and wide chemical space, with a public leaderboard enabling community adoption and sustained progress. Its applications (autonomous computational drug design) are high-value and cross-cut chemistry, biology, and AI/tool-use agent research. Paper 1 is timely and rigorous for clinical LLM evaluation, but is narrower in domain scope and primarily diagnostic/benchmarking rather than directly enabling discovery pipelines.