Large language models (LLMs) are rapidly transforming healthcare by supporting clinical documentation, decision-making, patient engagement, and biomedical research, with models like Med-PaLM and Med-Gemini demonstrating performance comparable to medical experts on standardized tasks. Despite promising advancements, critical challenges such as hallucinations, bias, privacy risks, regulatory uncertainty, and inequitable performance across diverse populations persist. To mitigate these risks, retrieval-augmented generation (RAG) has emerged as a key strategy to improve factuality, enable source attribution, and ensure alignment with up-to-date, local, and authoritative knowledge. Implementation frameworks emphasize human-in-the-loop oversight, rigorous validation, robust data governance, and continuous monitoring, particularly as regulatory bodies like the FDA and the EU with the AI act advance lifecycle guidance for AI as a medical device. While early deployments in ambient scribing, conversational AI, and clinical decision support show measurable gains in efficiency and clinician satisfaction, evidence on hard clinical outcomes remains limited, underscoring the need for large-scale, multisite trials and standardized evaluation metrics that reflect real-world safety and efficacy. Responsible adoption requires a balanced approach that leverages technological benefits while prioritizing patient safety, equity, transparency, and regulatory compliance.