The paper introduces three interpretable metrics—Explanation Stability Index (ESI), Causal Faithfulness Score (CFS), and Perturbation Stability Score (PSS)—to assess the faithfulness of large language models (LLMs) in clinical decision support. Evaluating six LLMs on 150 MedQA-USMLE questions, the study finds that only 23.3% of cited clinical concepts are causally necessary, and that correct answers often have lower CFS than incorrect ones, with answer consistency negatively predicting faithfulness. These findings suggest that accuracy, consistency, and consensus are incomplete safety signals for clinical decision-making support.
arXiv:2608. 09080v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved strong performance in medical question answering and clinical reasoning tasks.
The study evaluates large language models (LLMs) on unprocessed electronic medical record data for clinical registry abstraction, focusing on the American College of Cardiology National Cardiovascular Data Registry. In a pilot at one academic center, the LLM identified candidate data sources for each registry question, which abstractors used to define question‑specific document sets. In a subsequent validation at a second center, the LLM answered 157 registry questions with an overall mean accuracy of 91.5%, but accuracy dropped from 96% for simple medication or event flag questions to 62% for event timing questions, reflecting increasing ambiguity and required clinical reasoning.
LogiMed‑RoB is a new benchmark that tests large language models (LLMs) on hierarchical logical consistency in medical risk‑of‑bias assessments, using 860 randomized controlled trials and 14,820 queries based on Cochrane Risk of Bias 2.0 expert logic. The benchmark evaluates models across four dimensions—Atomic Consistency, Domain Consistency, Aggregation Consistency, and Evidential Faithfulness—revealing a catastrophic error‑compounding effect where high atomic accuracy does not translate to end‑to‑end consistency. Experiments on ten state‑of‑the‑art LLMs show that even top models can fail to deduce correct outcomes in a significant portion of cases, highlighting a gap between evidence retrieval and reasoning. whyItMatters":"The study shows that high outcome accuracy can mask critical reasoning flaws, emphasizing the need for white‑box logical verification before deploying LLMs in clinical settings."
The paper investigates how misleading context—specifically fabricated evidence and bare assertions—affects large language models’ medical question‑answering performance. Experiments on MedMisBench show that models are more prone to adopt answers based on assertions than fabricated evidence, and that these misleading cues are often disclosed in reasoning traces but rarely in final responses. A monitor that reads open reasoning traces can detect most corrupted decisions, whereas monitoring only responses is less effective.
arXiv:2606. 07237v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used in healthcare for tasks such as clinical question answering, diagnosis support, and report summarization.
arXiv:2608. 03028v1 Announce Type: new Abstract: Applying a valid medication-safety rule when its patient-specific conditions are not met can produce an incorrect decision.
The paper evaluates uncertainty estimation (UE) methods for clinical vision‑language models (VLMs) on visual question answering (VQA). Across 8 UE techniques and 12 VLMs, UE quality tracks model accuracy, degrading where performance is weakest, and fails to signal uncertainty when models are stressed by hiding the correct answer (NOTA perturbations). However, UE on unperturbed inputs reliably predicts which predictions will collapse under NOTA, suggesting UE can diagnose model fragility.
The study evaluates whether the chain-of-thought (CoT) rationales produced by medical language models truly influence their answers. Using a 30‑operator perturbation audit that modifies both the question and the CoT (e.g., severity reversal, negation flip, demographic swap, evidence ablation), the authors found that 72.9% of edits did not change the model’s answer—a high Chain‑Decoupling Rate (CDR). Across 14 large language models and four medical QA benchmarks, the CoT text had little impact on accuracy, and removing CoT prompting did not reduce performance. "whyItMatters":"The findings suggest that current medical CoT outputs may be more documentation than genuine reasoning, highlighting the need for better faithfulness checks in clinical AI systems."
arXiv:2609.32807v2 Announce Type: replace Abstract: Clinical LLM evaluation often emphasizes answer accuracy; however, accuracy alone does not test counterfactual consistency or demographic robustnes...
arXiv:2609.24799v1 Announce Type: new Abstract: Post-training quantization (PTQ) enables efficient deployment of large language models, and PTQ methods are usually optimized and evaluated with generi...
arXiv:2607. 28677v1 Announce Type: new Abstract: LLM now pass medical licensing examinations and, in curated cases, can rival physicians at diagnostic reasoning.
The UIC-AIHealth4All system was presented for the ArchEHR-QA 2026 shared task on grounded question answering from electronic health records. It participated in evidence identification, answer generation, and answer‑evidence alignment, using an answer‑first pipeline that generates candidate answers with cited note sentences before classifying the full evidence set. The system ranked third in evidence identification, ninth in answer generation, and fifth in answer‑evidence alignment, and a linguistic analysis showed its outputs were harder to read than clinician‑authored references, highlighting the need for readability optimization in clinical NLP.