It is suggested that trustworthy clinical decision support should be evaluated by both average correctness and stability across medically equivalent patient narratives.
Abstract
Medical large language models are often judged by how many clinical questions they answer correctly. That view is useful, but it misses a practical risk. A model may know the right answer and still change its response when the same case is written in a different patient voice. This paper evaluates that risk as SDoH aware narrative anchoring bias. We use NarrativeShield SDoH MedQA, a counterfactual medical question answering dataset in which each case appears in persona based narratives while the answer key remains fixed. The dataset is reshaped from wide format into case grouped persona rows. We evaluate three open source instruction tuned LLMs from the Qwen2.5 family: 1.5B, 3B, and 7B. The final experiment uses 300 clinical cases and produces 8,100 model responses across three prompting conditions. We report persona level accuracy, counterfactual consistency, correct consistency, and narrative sensitivity error. Qwen2.5 7B achieves the best accuracy at 56.33 percent and the best correct consistency at 40.33 percent. Paired McNemar exact tests show significant accuracy gains for 7B over 3B in all prompt settings. Even so, narrative sensitivity remains, with the lowest error still at 31.67 percent. These results suggest that trustworthy clinical decision support should be evaluated by both average correctness and stability across medically equivalent patient narratives.
An evaluation framework based on the MedMCQA dataset consisting of two complementary uncertainty settings is proposed, which introduces linguistic uncertainty cues through prompt modifications to simulate ambiguous clinical contexts and observes significant variation across models in their ability to abstain when the correct answer is unavailable.
Maryam Tahermazandarani, Adnan Mahmood, Fahmida Islam et al.· International Conference on...· 0 citations
NarrativeShield, a three-agent pipeline that structurally extracts and verifies clinical facts before diagnostic reasoning begins, reducing the Narrative Anchoring Gap to near-zero and achieving the lowest rate of severely unstable decisions of any method across all models, at a modest and mechanistically expected accuracy cost.
Prabhjot Singh, Pritam Deka, V. Chennareddy· arXiv.org· 0 citations
LLM-judged clinical safety effects should be reported as directional and relative, anchored to human review and evaluated jointly with helpfulness, not as calibrated absolute rates.
Medical LLMs are often evaluated by whether they select the correct diagnosis, but diagnostic accuracy alone does not show whether the model used the case evidence appropriately. We present a behavioral audit of evidence use in medical diagnosis. For each case, we decompose patient information into evidence units, score candidate diagnoses under controlled evidence subsets, and mine low-order interactions in diagnostic margins. Because medical evidence is diagnosis-relative, the audit separates interaction discovery from failure assignment: large or negative interactions can reflect plausible differential diagnosis, while suspicious interactions require robustness checks and clinical review. We evaluate five open-weight LLMs on DDXPlus, CupCase, and MedCase. Across datasets, faithful support and differential conflict or cancellation account for most interaction strength, showing that many evidence interactions are clinically plausible rather than failures. In a DDXPlus-focused blinded five-reviewer 130-item enriched review sample, invalid or shortcut-like cases concentrate in negated or absent findings and clinically local evidence. These results show that accuracy can hide candidate evidence-use failures and motivate role-aware audits for medical LLM evaluation.
Five widely used prompting strategies across five influential LLMs in the latest medical bias benchmark reveal substantial heterogeneity in both effectiveness and overhead across models, with no strategy proving universally effective and some even exacerbating bias.
Ying Xiao, Zhenpeng Chen, Jie M. Zhang· Philosophical transactions....· 1 citation
Clinical decisions, such as diagnosing conditions, prescribing medications, and recommending procedures, are rarely made with absolute certainty. Instead, they reflect probabilistic judgments shaped by evolving patient information and incomplete evidence. However, current EHR systems and knowledge graphs encode such decisions as deterministic triples, lacking a mechanism to represent the subjective confidence inherent in clinical reasoning. We present JudgEHR, a framework for clinical decision confidence estimation that leverages large language models (LLMs) to perform cohort-based collective inference over structured patient records by representing clinical events as knowledge graph triples and integrating them into LLM prompts. JudgEHR groups related clinical concepts into cohorts using LLM-driven relational inference, and then jointly evaluates the plausibility of all clinical decision triples within each cohort by considering patient visit history and background medical knowledge. We apply our method to the MIMIC-III dataset. Our statistical analysis shows that JudgEHR generates semantically consistent confidence scores, with similar concepts receiving closer values, whereas dissimilar replacements yield large confidence differences. Experiments on the MIMIC-III dataset show that incorporating the confidence into a zero-shot LLM-based pipeline improves relative AUROC by ${1 4. 6 \%}$ and AUPRC by 21.8% on the mortality prediction task.
Kexuan Xin, Guillaume Pelat, Jonathan Vitale et al.· International Conference on...· 0 citations
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