Skip to content
Log in
Sponsored AI AM CLAUDE, A Beginner's Guide: Learn the basics of working with AI AI AM CLAUDE, A Beginner's Guide: Learn the basics of working with AI
Review Open access

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

N. Dashti M. Schneider J. Eckardt F. Fiebig D. Schweigler S. Buttner J. Middeke M. Bornhauser C. Rollig J. N. Kather I. Wiest
Aug 2026 · medRxiv · 0 citations
Medicine

TL;DR

Evaluation across three AML clinical trials showed that strict LLT-level string agreement underestimated clinical ap-propriateness, highlighting the importance of combining hierarchical evaluation metrics with clini-cal expert validation for AI-assisted MedDRA coding in hematology trials.

DOI Semantic Scholar
Read PDF

Similar papers

Open access Aug 2026

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...

Junlong Ma, Xue-Hong Wu, Zeying Feng et al. · 0 citations
Review 2026

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

It is demonstrated that modern LLMs with reasoning capabilities can effectively support real-time clinical evidence synthesis without task-specific fine-tuning, offering a pathway toward scalable automated systems for clinical trial interpretation that could substantially reduce the evidence-to-practice gap in medical...

Shobanapriyan Chandrasegaran, Amal Htait · 0 citations
#large language models Review Open access Sep 2026

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

LLMs delivered promising results as assistive tools for RCA in radiation oncology, with the ability to generate relevant and accurate analyses aligned with expert expectations.

Yun-Tao Wang, M. De Ornelas, M. T. Studenski et al. · 0 citations
Conference Open access Sep 2026

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

MERGE-Med is a hybrid multi-expert architecture that consists of a mixture-of-experts (MoE) layer with three domain-specialized LLaMA-3 8B models for diagnostic reasoning, treatment protocol generation, and patient-friendly communication and a contrastive fine-tuning framework validated by an ensembled DeBERTa-v3-large...

D. S. Tejaswi, P. R. Reddy, K. Shailaja et al. · 0 citations
#artificial intelligence Preprint Sep 2026

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

RG-ICL is introduced, a training-free inference framework that retrieves query-aligned demonstrations using frozen encoders, without task-specific parameter updates, and indicates that for medical image interpretation, curating which reference cases an MLLM sees is a practical alternative to retraining it.

Min-Da Zhao, Fang-Yu Hu, Yan Luo et al. · 0 citations
Open access Aug 2026

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

Combining LLM-based clinical interpretation with deterministic terminology verification produces auditable, database-ready code sets, but retrieval and broad family expansion do not consistently improve exact-code performance.

A. Habibdoust, A. Sajjad, D. Hernández et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.