MedSafetyBench: Evaluating and Improving the Medical Safety of Large Language Models
Tessa Han, Aounon Kumar, Chirag Agarwal, Himabindu Lakkaraju

TL;DR
This paper introduces MedSafetyBench, a benchmark dataset for evaluating and enhancing the medical safety of large language models (LLMs), revealing current models' safety deficiencies and demonstrating improvements through fine-tuning.
Contribution
It defines medical safety for LLMs based on medical ethics principles and provides the first benchmark dataset to systematically assess and improve their safety in medical contexts.
Findings
Publicly available medical LLMs do not meet safety standards.
Fine-tuning with MedSafetyBench improves LLMs' medical safety.
The benchmark enables systematic safety evaluation in medical AI.
Abstract
As large language models (LLMs) develop increasingly sophisticated capabilities and find applications in medical settings, it becomes important to assess their medical safety due to their far-reaching implications for personal and public health, patient safety, and human rights. However, there is little to no understanding of the notion of medical safety in the context of LLMs, let alone how to evaluate and improve it. To address this gap, we first define the notion of medical safety in LLMs based on the Principles of Medical Ethics set forth by the American Medical Association. We then leverage this understanding to introduce MedSafetyBench, the first benchmark dataset designed to measure the medical safety of LLMs. We demonstrate the utility of MedSafetyBench by using it to evaluate and improve the medical safety of LLMs. Our results show that publicly-available medical LLMs do not…
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TopicsMachine Learning in Healthcare
Methods7 Fastest Ways to Call American Airlines Reservations Number (USA Guide) · Sparse Evolutionary Training
