Safety challenges of AI in medicine in the era of large language models

LLMs can produce convincing but false medical information: The paper identifies that large language models in medicine face "LLM-specific safety challenges" including issues with accuracy and reliability across data collection, model training, and real-world application—meaning these systems can generate plausible-sounding medical advice that may be incorrect, posing direct risks to patient safety.

Trust is the critical barrier to medical AI adoption: The authors emphasize that "safety issues of using AI in clinical practice and healthcare system operation would undermine trust among patient, clinicians and the public," identifying public concerns over AI safety as "the most significant obstacles to the adoption of AI in medicine"—suggesting that without addressing safety comprehensively, even capable AI tools won't be used.

Medical AI faces both unique and shared safety problems: The paper distinguishes between challenges specific to LLMs (like their natural language interface creating new communication risks) and broader AI safety issues that affect all medical AI systems, indicating that solutions must address multiple layers of risk rather than treating LLMs as simply a faster version of existing AI.

Safety must be built into AI development from the start: By examining risks "across data collection, model training, and real-world application," the paper suggests that safety cannot be added as an afterthought but requires careful oversight at every stage of how medical AI systems are built and deployed.