From Hallucinations to Help: Can Retrieval‑Augmented Generation (RAG) Deliver Trustworthy Clinical Artificial Intelligence?

KEY MESSAGES- Standalone AI systems risk clinical harm due to inaccuracies (“hallucinations”) and biases, limiting their reliability for diagnosis or documentation.- Retrieval-augmented generation (RAG) improves safety by grounding AI outputs in real-time medical evidence, but its success hinges on high-quality data and equitable design.- Policymakers must prioritize adaptive regulation, including standardized bias audits, interoperability standards, and global access to prevent AI from exacerbating healthcare disparities.- Clinicians, not AI, must retain final authority; RAG tools should augment judgment with explainable, verifiable recommendations while minimizing workflow disruption.