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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.00467 |
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| _version_ | 1866909684954300416 |
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| author | Balazadeh, Vahid Cooper, Michael Pellow, David Assadi, Atousa Bell, Jennifer Coatsworth, Mark Deshpande, Kaivalya Fackler, Jim Funingana, Gabriel Gable-Cook, Spencer Gangadhar, Anirudh Jaiswal, Abhishek Kaja, Sumanth Khoury, Christopher Krishnan, Amrit Lin, Randy McKeen, Kaden Naimimohasses, Sara Namdar, Khashayar Newatia, Aviraj Pang, Allan Pattoo, Anshul Peesapati, Sameer Prepelita, Diana Rakova, Bogdana Sadatamin, Saba Schulman, Rafael Shah, Ajay Shah, Syed Azhar Shah, Syed Ahmar Taati, Babak Unnikrishnan, Balagopal Urteaga, Iñigo Williams, Stephanie Krishnan, Rahul G |
| author_facet | Balazadeh, Vahid Cooper, Michael Pellow, David Assadi, Atousa Bell, Jennifer Coatsworth, Mark Deshpande, Kaivalya Fackler, Jim Funingana, Gabriel Gable-Cook, Spencer Gangadhar, Anirudh Jaiswal, Abhishek Kaja, Sumanth Khoury, Christopher Krishnan, Amrit Lin, Randy McKeen, Kaden Naimimohasses, Sara Namdar, Khashayar Newatia, Aviraj Pang, Allan Pattoo, Anshul Peesapati, Sameer Prepelita, Diana Rakova, Bogdana Sadatamin, Saba Schulman, Rafael Shah, Ajay Shah, Syed Azhar Shah, Syed Ahmar Taati, Babak Unnikrishnan, Balagopal Urteaga, Iñigo Williams, Stephanie Krishnan, Rahul G |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_00467 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Red Teaming Large Language Models for Healthcare Balazadeh, Vahid Cooper, Michael Pellow, David Assadi, Atousa Bell, Jennifer Coatsworth, Mark Deshpande, Kaivalya Fackler, Jim Funingana, Gabriel Gable-Cook, Spencer Gangadhar, Anirudh Jaiswal, Abhishek Kaja, Sumanth Khoury, Christopher Krishnan, Amrit Lin, Randy McKeen, Kaden Naimimohasses, Sara Namdar, Khashayar Newatia, Aviraj Pang, Allan Pattoo, Anshul Peesapati, Sameer Prepelita, Diana Rakova, Bogdana Sadatamin, Saba Schulman, Rafael Shah, Ajay Shah, Syed Azhar Shah, Syed Ahmar Taati, Babak Unnikrishnan, Balagopal Urteaga, Iñigo Williams, Stephanie Krishnan, Rahul G Computation and Language Artificial Intelligence 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. |
| title | Red Teaming Large Language Models for Healthcare |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2505.00467 |