Retrieval-Augmented Large Language Models for Clinically Aligned Adverse Event Coding in Acute Myeloid Leukemia Clinical Trials

Intelligent Framework for Adverse Drug Event Identification Using Large Language Models and Retrieval-Augmented Generation: Development and Evaluation Study

Synergizing a curated domain-specific knowledge base with LLMs via a RAG architecture is an effective strategy for accurately identifying ADEs in unstructured Chinese clinical notes, providing a foundational open-source benchmark and a robust technical framework to advance pharmacovigilance, drug safety research, and c...

Enhancing Clinical Trial Analysis through Large Language Models for Multi-Evidence Natural Language Inference

Augmenting patient safety surveillance in radiation oncology with large language model-based root cause analysis: A proof-of-concept study

We investigated the potential utility of large language models (LLMs) in supporting patient safety efforts. Specifically, we evaluated the reasoning capabilities of LLMs in performing root cause analysis (RCA) of radiation oncology incidents using narrative reports from the Radiation Oncology Incident Learning System (...

MERGE-Med: Multi-Expert Retrieval-Grounded Engine for Medical Report Summarization Using Hierarchical RAG and Contrastive Learning

Electronic health records (EHRs) have created large volumes of clinical data that require automated and accurate summarization that patients can understand. Current large language models (LLMs) are prone to hallucination and show limited task specialization and inadequate clinical grounding. MERGE-Med is a hybrid multi...

Representation-guided in-context learning for medical image interpretation with multimodal large language models

Medical image interpretation is central to diagnosis and care, yet adapting general-purpose multimodal large language models (MLLMs) often requires resource-intensive domain-specific fine-tuning. Here we introduce representation-guided in-context learning (RG-ICL), a training-free inference framework that retrieves que...

TrialCode Agent: LLM-Assisted Clinical Code-Set Construction for Trial Emulation

Objective Translating free-text clinical trial criteria into computable code sets is a valuable standardization practice that is necessary for producing reproducible real-world evidence studies but requires standardized interpretation across multiple clinical vocabularies. Methods We developed TrialCode Agent, a hybrid...