# Red Teaming Large Language Models for Healthcare

arXiv: https://arxiv.org/abs/2505.00467 · HTML: https://mlsanity.com/paper/2505.00467/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Safety alignment, refusal and guardrails, one of 12 directions in LLM safety and security.
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 1 May 2025.

## Placed in

- LLM safety and security / [Safety alignment, refusal and guardrails](https://mlsanity.com/safety/safety-alignment-refusal-and-guardrails/)

## Builds on (most cited first, 7 of 7)

- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://mlsanity.com/paper/2311.05232/) (2023)
- [Red-Teaming for Generative AI: Silver Bullet or Security Theater?](https://mlsanity.com/paper/2401.15897/) (2024)
- [Against The Achilles' Heel: A Survey on Red Teaming for Generative Models](https://mlsanity.com/paper/2404.00629/) (2024)
- [Evaluation of GPT-3.5 and GPT-4 for supporting real-world information needs in healthcare delivery](https://mlsanity.com/paper/2304.13714/) (2023)
- [Lessons From Red Teaming 100 Generative AI Products](https://mlsanity.com/paper/2501.07238/) (2025)
- [Walking a Tightrope -- Evaluating Large Language Models in High-Risk Domains](https://mlsanity.com/paper/2311.14966/) (2023)
- [Beyond Accuracy: Investigating Error Types in GPT-4 Responses to USMLE Questions](https://mlsanity.com/paper/2404.13307/) (2024)

## Built on by (most cited first, 2 of 2)

- [ResidencyRL: Reinforcement Learning in Simulated Clinical Environments](https://mlsanity.com/paper/2608.07418/) (2026)
- [A Multi-Domain Red Teaming Framework for Safety, Robustness, and Fairness Evaluation of Medical Large Language Models](https://mlsanity.com/paper/2606.00027/) (2026)

## Abstract (arXiv)

We present the design process and findings of the pre-conference workshop at the Machine Learning for Healthcare Conference (2024) entitled Red Teaming Large Language Models for Healthcare, which took place on August 15, 2024. Conference participants, comprising a mix of computational and clinical expertise, attempted to discover vulnerabilities -- realistic clinical prompts for which a large language model (LLM) outputs a response that could cause clinical harm. Red-teaming with clinicians enables the identification of LLM vulnerabilities that may not be recognised by LLM developers lacking clinical expertise. We report the vulnerabilities found, categorise them, and present the results of a replication study assessing the vulnerabilities across all LLMs provided.