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:2608.30022v1 Announce Type: new Abstract: Introduction: NICE guidelines provide evidence-based recommendations for clinical care but remain largely in unstructured natural language. Existing ap...
arXiv:2607. 13036v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for decision support in healthcare, but clinical evidence is often incomplete or evolving.
arXiv:2606. 03157v1 Announce Type: new Abstract: Large language models (LLMs) have been widely adopted in healthcare, yet they still encounter significant challenges in complex clinical decision-making scenarios.
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%.
The paper investigates how medical large language models (LLMs) may exhibit narrative anchoring bias when presented with the same clinical case in different patient voices. Using the NarrativeShield SDoH MedQA dataset, the authors evaluate three Qwen2.5 instruction‑tuned LLMs (1.5B, 3B, 7B) on 300 clinical cases, reporting metrics such as persona‑level accuracy, counterfactual consistency, correct consistency, and narrative sensitivity error. The 7B model achieves the highest accuracy (56.33 %) and correct consistency (40.33 %), yet narrative sensitivity errors remain substantial (31.67 %).
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:2604. 05435v2 Announce Type: replace Abstract: Incomplete or inconsistent discharge documentation drives care fragmentation and avoidable readmissions.
arXiv:2609.00296v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly proposed for healthcare tasks such as clinical documentation, evidence retrieval, patient messaging,...
arXiv:2608.20887v1 Announce Type: cross Abstract: Automatic Medical Coding (AMC), which assigns standardized International Classification of Diseases (ICD) codes to clinical notes, is essential for m...
arXiv:2607. 06802v1 Announce Type: cross Abstract: Open physiological corpora are heterogeneous: they use different sensors, labels, sampling rates, recording settings, and clinical endpoints.
arXiv:2607. 24371v1 Announce Type: cross Abstract: Healthcare interoperability requires AI systems to produce structured outputs conforming to standardized schemas including ICD-10 for diagnostic coding, CPT for procedure billing, and HL7 FHIR for data exchange.