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Résumé : Background: The integration of Large Language Models (LLMs) into healthcare is currently hindered by the critical risk of "hallucinations", the generation of plausible but factually incorrect medical information.Two primary adaptation strategies have emerged to address this: Fine-Tuning (FT), which updates model parameters to internalize domain knowledge, and Retrieval-Augmented Generation (RAG), which grounds model outputs in external evidence at inference time.Objective: This systematic review synthesizes findings from 50 peer-reviewed studies published between 2023 and 2025 to evaluate the comparative effectiveness of RAG versus Fine-Tuning in medical contexts.Results: Evidence consistently demonstrates that RAG is the superior strategy for clinical safety, significantly reducing hallucination rates by dynamically accessing up-to-date knowledge without the need for retraining.Conversely, while Fine-Tuning excels at adapting to domain-specific syntax and clinical reasoning styles, it incurs high computational costs and carries a significant risk of "catastrophic forgetting," where general knowledge is degraded.Conclusion: We conclude that while Fine-Tuning remains valuable for specialization, RAG provides the necessary factual guardrails for patient-facing applications.Future development should prioritize "Hybrid" frameworks that synergize the reasoning depth of Fine-Tuning with the factual integrity of RAG.