arXiv:2601. 22324v3 Announce Type: replace Abstract: Modern clinical practice relies on evidence-based guidelines implemented as compact scoring systems composed of a small number of interpretable decision rules.
arXiv:2606. 19183v1 Announce Type: cross Abstract: Large language models (LLMs) can make clinical decision support more accessible by interpreting free-text documentation, but their direct use as diagnostic engines is limited by sensitivity to prompts, information order, and plausible but incorrect outputs.
The study evaluates AI-generated summaries for cancer patients using a dual assessment framework that includes human experts and LLM-as-a-judge. Human domain experts—oncology clinicians and patient-facing care staff—assess summary quality on accuracy, clinical relevance, and readability. The research identifies limitations such as omissions and minor inaccuracies, which are then used to iteratively refine prompts, grounding, and safety guardrails.
The paper introduces a retrieval‑augmented multi‑agent framework that automatically generates instance‑specific evaluation rubrics for medical language models. By retrieving authoritative medical evidence, decomposing it into atomic facts, and combining these with user interaction constraints, the system produces fine‑grained criteria that outperform GPT‑4o on HealthBench and LLMEval‑Med. The generated rubrics also guide response refinement, improving medical LLM output quality by 9.2%.
arXiv:2605. 30295v2 Announce Type: replace-cross Abstract: Large language models (LLMs) show promise for clinical reasoning and decision support, but evaluation in realistic, electronic health record-congruent settings remains limited.
arXiv:2606. 09852v1 Announce Type: cross Abstract: High-quality source code documentation is vital yet often neglected, especially in critical domains like healthcare where reliability and maintainability are essential.
arXiv:2607. 08602v1 Announce Type: new Abstract: Hepatocellular carcinoma (HCC) is a common malignancy and a leading cause of cancer-related mortality.
arXiv:2607. 04907v1 Announce Type: new Abstract: Deploying Large Language Models (LLMs) in high-stakes clinical settings remains limited by structural hallucinations, weak deterministic reasoning over tabular patient data, and omissions in vector retrieval.
The paper introduces MEGA-CDP, a benchmark designed to evaluate medical large language models (LLMs) on their ability to generate clinical decision pathways (CDPs) that adhere to clinical practice guidelines. MEGA-CDP is built from 2,274 English and Chinese guidelines, producing 42,353 clinical cases with explicit reference CDPs, and supports both single-turn and multi-turn interactions. Experiments on 16 LLMs reveal that reliable guideline adherence remains difficult, underscoring the need for CDP-focused evaluation and the potential of MEGA-CDP to advance medical LLM performance.
arXiv:2606. 19852v1 Announce Type: cross Abstract: Information extraction from pathology reports is essential for cancer staging, tumor registry population.
arXiv:2608.28974v1 Announce Type: new Abstract: Clinically relevant oncology information is distributed across heterogeneous, longitudinal documentation, creating substantial abstraction burden and r...
arXiv:2509. 21530v2 Announce Type: replace Abstract: Data augmentation is a widely used strategy to improve model robustness and generalization by enriching training datasets with synthetic examples.