The capabilities of large language models (LLMs) have been progressing at a
breathtaking speed, leaving even their own developers grappling with the depth
of their potential and risks. While initial steps have been taken to evaluate
the safety and alignment of general-knowledge LLMs, exposing some weaknesses,
to our knowledge, the safety and alignment of medical LLMs has not been
evaluated despite their risks for personal health and safety, public health and
safety, and human rights. To this end, we carry out the first safety evaluation
for medical LLMs. Specifically, we set forth a definition of medical safety and
alignment for medical artificial intelligence systems, develop a dataset of
harmful medical questions to evaluate the medical safety and alignment of an
LLM, evaluate both general and medical safety and alignment of medical LLMs,
demonstrate fine-tuning as an effective mitigation strategy, and discuss
broader, large-scale approaches used by the machine learning community to
develop safe and aligned LLMs. We hope that this work casts light on the safety
and alignment of medical LLMs and motivates future work to study it and develop
additional mitigation strategies, minimizing the risks of harm of LLMs in
medicine.

本文首次对医学 LLMs 进行了安全评估，讨论了医学 LLMs 的安全和对齐性，并展示了微调作为有效的缓解策略，希望这项工作能够启发未来的研究，并开发出更多的缓解策略，以减少医学 LLMs 在医学领域中的潜在风险。