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The study introduces NPC‑RubricBench, a phase‑aware benchmark that evaluates large language models (LLMs) on multistage clinical reasoning throughout the nasopharyngeal carcinoma care continuum. Using longitudinal data from 405 patients, the benchmark contains 941 patient‑phase units and 17,688 patient‑specific criteria across five dimensions, focusing strictly on decision‑time evidence. Evaluation of 30 general‑purpose and medical LLMs shows that strong diagnostic accuracy does not translate into better downstream care, with failure modes shifting from explicit decision errors in staging to decision omissions in initial work‑up and follow‑up, and reveals a trade‑off between prompt granularity, decision quality, evidence faithfulness, and safety adherence.
arXiv:2608.28592v1 Announce Type: new Abstract: Large language models (LLMs) achieve high scores on medical knowledge examinations, yet real-world oncology is not a knowledge test--it is a sequence o...
arXiv:2607. 08602v1 Announce Type: new Abstract: Hepatocellular carcinoma (HCC) is a common malignancy and a leading cause of cancer-related mortality.
arXiv:2607. 10275v1 Announce Type: new Abstract: Large language models achieve high scores on medical knowledge assessments, yet clinical reasoning requires actively deciding what to investigate under uncertainty.
arXiv:2607. 28677v1 Announce Type: new Abstract: LLM now pass medical licensing examinations and, in curated cases, can rival physicians at diagnostic reasoning.
The paper introduces AI Consilium, a benchmark for evaluating large language models (LLMs) on providing clinically grounded, coherent, actionable, and safe second opinions in multidisciplinary settings. Using 300 NEJM–MedQA cases, the study compares eight single-model pipelines that generate role-specific opinions and interactive-consilium reports under free-form and structured conditions. Results show that structured final reports generally score higher, especially in management, and that GPT‑5.6‑Sol outperforms other models across all evaluated components, demonstrating the benchmark’s ability to differentiate model capabilities and the impact of response structure.