Fine-Tuning, Retrieval-Augmented Generation, and Hybrid Adaptation of Language Models for Clinical Decision-Making in Health Care: Systematic Review
Abstract
Background: Large language models (LLMs) demonstrate strong performance on medical knowledge benchmarks, but their safe and effective use in clinical practice depends on posttraining adaptation rather than raw model capability. Fine-tuning, retrieval-augmented generation (RAG), and hybrid approaches are principal strategies for grounding language models in clinical evidence, yet their comparative effectiveness remains unclear.
Objective: This systematic review aims to synthesize evidence on fine-tuning, RAG, and hybrid posttraining strategies for clinical diagnosis and decision-support tasks and to identify strategy-task alignments and methodological features associated with improved performance.
Methods: We conducted a systematic review in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. PubMed/MEDLINE, Scopus, and Web of Science were searched from January 2018 through May 2026. Eligible studies evaluated transformer-based language models that underwent posttraining adaptation, retrieval augmentation, or both for clinical decision support, diagnosis, triage, risk stratification, or related health care applications. Studies evaluating nonadapted models, non-language-model AI systems, prompt engineering without performance evaluation, or nonclinical applications were excluded. Data extracted included model architecture, adaptation strategy, clinical domain, validation approach, and performance outcomes. Risk of bias…
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