Role prompting modulates linguistic style but not clinical decision structure in GPT-5 tumour board simulation

A multidimensional benchmarking framework for large language models in oncologic decision making

A multi-dimensional evaluation framework integrating clinical quality and operational efficiency provides more actionable insights than single metric assessments, enabling pragmatic model selection for oncology practice.

Assessing the performance of three large language models in thyroid cancer tumour board decision-making.

Decoding high-order clinical correlations: a knowledge-driven large language model framework for specialized medical decision-making

Embedding domain-specific knowledge into LLMs may improve performance on specialized exam-style thoracic-surgery questions on this text-only benchmark, however, the present 56-item evaluation does not establish clinical equivalence, diagnostic accuracy in practice, multimodal competence, or readiness for real-world clinical decision support.

Concordance Between Clinical Practice Recommendations Generated by Generative Artificial Intelligence and the Vía RICA 2026 Enhanced Recovery Guideline: A Proof-of-Concept Study Using a Closed Evidence Corpus

Clinical practice guidelines require expert synthesis that large language models (LLMs) might partly automate, yet their ability to reproduce clinically actionable recommendations is poorly quantified. We evaluate an LLM (Claude Sonnet 4.6) against the 103 recommendations of the Spanish enhanced-recovery guideline Vía RICA 2026, grouped in 17 bundles. The model used the panel’s own closed corpus (617 documents) in a multilingual retrievalaugmented generation pipeline. Concordance was assessed twice: by optimal 1:1 bipartite matching (Hungarian) on cosine similarity, and by an LLM-as-a-judge clinical adjudicator (Claude Haiku 4.5) validated against a three-clinician panel (Fleiss’ κ = 0.538). The two schemes bracket a micro F1 of 0.61–0.69 and reveal four findings: (i) a systematic granularity bias, producing 1–8 recommendations per bundle regardless of ground-truth size; (ii) failure of cosine similarity to discriminate within narrow clinical domains; (iii) high reference-concordance precision (0.70–0.81) despite low exhaustiveness; and (iv) no transfer of the GRADE fields, evidence level agreeing no better than chance and strength systematically downgraded. An eight-fold larger retrieval budget left it intact. A corpus audit found 25 documents that formulate recommendations; excluding them lowers judged micro F1 to 0.602. The results delimit the current utility of generative AI for guideline development.

Explainability of decoder-only clinical large language models: A scoping review.

Findings show that clinical LLM explainability has shifted toward fluent generative rationales, but evidence that such explanations reflect model reasoning remains limited, and three regulatory priorities are highlighted: prioritizing explanations that enable independent verification or logic auditing over plausibility-only rationales; preferring inspectable models where regulatory documentation is required; and prospectively validating explanations in clinical workflows before scaling.

DSAI-12 ACCURACY, SAFETY, AND READABILITY OF PUBLIC-FACING LARGE LANGUAGE MODELS IN CNS METASTASIS

Although contemporary LLMs increasingly reflect medical consensus for CNS metastases, inconsistent reliability remains a concern, underscoring the need for caution in patient use.