TL;DR
MedAL is a scalable method for fine-tuning LLMs across many health systems without sharing patient-level data, enabling high-performance local models for reasoning over clinical notes and may also be useful for training multimodal healthcare AI models.
CoMedBench is introduced, a reproducible benchmark that evaluates a family of generators under a common clinical-validity framework and one shared training and evaluation engine, spanning static tabular and temporal downstream tasks on established critical-care datasets.
A robust technical framework is established for developing trustworthy, high-efficiency medical AI systems capable of operating entirely within hospital-controlled infrastructure by evaluating the synergistic impact of context window scaling and multi-stage supervised fine-tuning within a localized Retrieval-Augmented...
Findings suggest that drift-triggered temporal and institutional adaptation can improve the robustness and stability of clinical language models under the distribution shifts represented in the evaluated datasets, but further prospective and independent external validation is required before the framework can be consid...
OmniMed-FL is presented, a controlled systems study of multimodal federated learning for five-class clinical condition classification (Normal, Pneumonia, COVID-19, Pleural Effusion, Cardiomegaly) and benchmarks eight fusion strategies, three initializations, four missing-text imputation rules, and matched federated bas...
A comprehensive framework for transforming LLMs into trustworthy medical specialists is presented, and the evolution toward agentic AI systems that orchestrate them for autonomous, collaborative clinical decision-making is explored.
This work introduces and evaluates a privacy-preserving knowledge distillation framework for LLM-based clinical modeling, using multimorbidity scoring as a healthcare task, and establishes a trustworthy, privacy-compliant pathway for large-scale healthcare applications of LLMs.
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