Multi-Large Language Model Orchestrated Severity Assessment of Clinical Records (MOSAIC)

Less can be better: decomposing clinical data modalities in large language model-based healthcare applications.

CardioBench: A Real-World Data Benchmark for Evaluating Large Language Models in Clinically Authentic Cardiovascular Care Scenarios

CardioBench is the largest real-world, multi-task benchmark for LLM evaluation across the cardiovascular care continuum and offers the broadest coverage of clinically authentic cardiology scenarios reported to date.

A Structured Debate-Mixture-of-Agents Framework for Complex Clinical Diagnostic Decision Support

Large language models (LLMs) show potential for medical tasks, but their single-turn question-answer format does not reflect how clinical diagnosis is performed in practice. As a result, they remain limited in complex diagnostic settings. We developed Debate-Mixture-of-Agents (DMoA), a novel multi-agent framework that structures role-based interaction to support iterative diagnostic reasoning. Base models and DMoA were evaluated on 297 rare disease cases and 1,719 challenging cases. Across both datasets, DMoA improved most likely diagnosis accuracy by 10.21 percentage points and safety rate by 11.36 percentage points over GPT-4o baseline. Ablation experiments showed that the gains were not simply due to the use of more models or longer outputs, but also reflected the contribution of the structured workflow. Further analyses examined how framework design, base model choice, and token budget affected performance. DMoA performed better with a 4*2 structure, stronger base models, and a larger token budget. These findings demonstrate the potential of DMoA for clinical tasks and suggest further investigation of multi-agent frameworks.

ClinLens: Towards Long-Horizon Coding Agents for Longitudinal Multimodal Clinical Data Science

CLINLENS is introduced, a benchmark of 200 executable tasks over five linked MIMIC resources spanning structured electronic health records, notes, electrocardiograms, chest radiographs, and echocardiograms, which exposes a substantial gap between runnable submissions and correct clinical analyses.

Benchmarking large language models for clinical data extraction from Portuguese medical notes in a university hospital

The findings support the feasibility of applying LLM-based natural language processing tools in resource-limited, non-English healthcare settings and should assess emerging high-parameter models and explore additional clinical domains.

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.