The promise of AI-assisted medicine hinges not on raw model power but on how intelligently a language model is adapted to the clinical environment. For health-conscious adults and the clinicians who serve them, this distinction matters enormously: an unadapted AI may hallucinate drug interactions or miss rare diagnoses, while a properly calibrated one could meaningfully close gaps in triage, diagnosis, and risk stratification.
This PRISMA-compliant systematic review, drawing from PubMed/MEDLINE, Scopus, and Web of Science across studies published between January 2018 and May 2026, evaluated three principal post-training adaptation strategies for transformer-based large language models (LLMs) in clinical settings: fine-tuning (updating model weights on domain-specific data), retrieval-augmented generation (RAG, which grounds model outputs in external knowledge bases at inference time), and hybrid approaches combining both. The review focused on clinical decision support, diagnosis, triage, and risk stratification applications, systematically extracting model architecture, adaptation strategy, validation approach, and performance outcomes. Key findings indicate that strategy-task alignment — matching the adaptation method to the specific clinical challenge — is a stronger predictor of performance than model scale alone, with hybrid approaches demonstrating particular advantages where both up-to-date factual grounding and nuanced reasoning are required.
This review arrives at a pivotal moment in clinical AI deployment. The broader literature has shown that raw benchmark scores on medical knowledge tests (USMLE-style exams, for instance) frequently fail to predict real-world clinical utility. The RAG framework addresses a longstanding weakness of static fine-tuned models — knowledge staleness — but introduces its own failure modes, including retrieval of irrelevant or low-quality documents. Hybrid systems mitigate these limitations but add architectural complexity and potential latency. A critical limitation of this evidence base is the persistent absence of prospective clinical trials: most included studies relied on retrospective datasets or simulated clinical tasks, making causal claims about patient outcome improvement premature. This review is best read as a high-quality directional signal rather than a deployment blueprint — incremental but meaningfully consolidating for the field.