Regulatory Science Innovation for Generative AI and Large Language Models in Health and Medicine: A Global Call for Action
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| Autores principales: | , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866910822366707712 |
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| author | Ong, Jasmine Chiat Ling Ning, Yilin Liu, Mingxuan Ma, Yian Liang, Zhao Singh, Kuldev Chang, Robert T Vogel, Silke Lim, John CW Tan, Iris Siu Kwan Freyer, Oscar Gilbert, Stephen Bitterman, Danielle S Liu, Xiaoxuan Denniston, Alastair K Liu, Nan |
| author_facet | Ong, Jasmine Chiat Ling Ning, Yilin Liu, Mingxuan Ma, Yian Liang, Zhao Singh, Kuldev Chang, Robert T Vogel, Silke Lim, John CW Tan, Iris Siu Kwan Freyer, Oscar Gilbert, Stephen Bitterman, Danielle S Liu, Xiaoxuan Denniston, Alastair K Liu, Nan |
| contents | The integration of generative AI (GenAI) and large language models (LLMs) in healthcare presents both unprecedented opportunities and challenges, necessitating innovative regulatory approaches. GenAI and LLMs offer broad applications, from automating clinical workflows to personalizing diagnostics. However, the non-deterministic outputs, broad functionalities and complex integration of GenAI and LLMs challenge existing medical device regulatory frameworks, including the total product life cycle (TPLC) approach. Here we discuss the constraints of the TPLC approach to GenAI and LLM-based medical device regulation, and advocate for global collaboration in regulatory science research. This serves as the foundation for developing innovative approaches including adaptive policies and regulatory sandboxes, to test and refine governance in real-world settings. International harmonization, as seen with the International Medical Device Regulators Forum, is essential to manage implications of LLM on global health, including risks of widening health inequities driven by inherent model biases. By engaging multidisciplinary expertise, prioritizing iterative, data-driven approaches, and focusing on the needs of diverse populations, global regulatory science research enables the responsible and equitable advancement of LLM innovations in healthcare. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_07794 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Regulatory Science Innovation for Generative AI and Large Language Models in Health and Medicine: A Global Call for Action Ong, Jasmine Chiat Ling Ning, Yilin Liu, Mingxuan Ma, Yian Liang, Zhao Singh, Kuldev Chang, Robert T Vogel, Silke Lim, John CW Tan, Iris Siu Kwan Freyer, Oscar Gilbert, Stephen Bitterman, Danielle S Liu, Xiaoxuan Denniston, Alastair K Liu, Nan Computers and Society Artificial Intelligence The integration of generative AI (GenAI) and large language models (LLMs) in healthcare presents both unprecedented opportunities and challenges, necessitating innovative regulatory approaches. GenAI and LLMs offer broad applications, from automating clinical workflows to personalizing diagnostics. However, the non-deterministic outputs, broad functionalities and complex integration of GenAI and LLMs challenge existing medical device regulatory frameworks, including the total product life cycle (TPLC) approach. Here we discuss the constraints of the TPLC approach to GenAI and LLM-based medical device regulation, and advocate for global collaboration in regulatory science research. This serves as the foundation for developing innovative approaches including adaptive policies and regulatory sandboxes, to test and refine governance in real-world settings. International harmonization, as seen with the International Medical Device Regulators Forum, is essential to manage implications of LLM on global health, including risks of widening health inequities driven by inherent model biases. By engaging multidisciplinary expertise, prioritizing iterative, data-driven approaches, and focusing on the needs of diverse populations, global regulatory science research enables the responsible and equitable advancement of LLM innovations in healthcare. |
| title | Regulatory Science Innovation for Generative AI and Large Language Models in Health and Medicine: A Global Call for Action |
| topic | Computers and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2502.07794 |