Is regulatory science ready for artificial intelligence?
Abstract Trust is key in AI for regulatory science, but its definition is debated. If AI models use different features yet perform similarly, which should be trusted? If scientific theories must be testable, how critical is explainability? At the Global Summit on Regulatory Science (GSRS24), regulat...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01596-0 |
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| _version_ | 1850201322995843072 |
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| author | Thomas Hartung Maurice Whelan Weida Tong Robert M. Califf |
| author_facet | Thomas Hartung Maurice Whelan Weida Tong Robert M. Califf |
| author_sort | Thomas Hartung |
| collection | DOAJ |
| description | Abstract Trust is key in AI for regulatory science, but its definition is debated. If AI models use different features yet perform similarly, which should be trusted? If scientific theories must be testable, how critical is explainability? At the Global Summit on Regulatory Science (GSRS24), regulators agreed that successful AI adoption requires ongoing dialogue, adaptability, and AI-trained personnel to harness its potential for regulatory responsibilities in the evolving 21st-century landscape. |
| format | Article |
| id | doaj-art-73f9bcd7d0da4e9f98ee141e8ecdb9d1 |
| institution | OA Journals |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-73f9bcd7d0da4e9f98ee141e8ecdb9d12025-08-20T02:12:02ZengNature Portfolionpj Digital Medicine2398-63522025-04-01811510.1038/s41746-025-01596-0Is regulatory science ready for artificial intelligence?Thomas Hartung0Maurice Whelan1Weida Tong2Robert M. Califf3Johns Hopkins University, Bloomberg School of Public Health and Whiting School of EngineeringEuropean Commission, Joint Research Center (JRC)National Center for Toxicological Research, US Food and Drug AdministrationOffice of Commissioner, US Food and Drug AdministrationAbstract Trust is key in AI for regulatory science, but its definition is debated. If AI models use different features yet perform similarly, which should be trusted? If scientific theories must be testable, how critical is explainability? At the Global Summit on Regulatory Science (GSRS24), regulators agreed that successful AI adoption requires ongoing dialogue, adaptability, and AI-trained personnel to harness its potential for regulatory responsibilities in the evolving 21st-century landscape.https://doi.org/10.1038/s41746-025-01596-0 |
| spellingShingle | Thomas Hartung Maurice Whelan Weida Tong Robert M. Califf Is regulatory science ready for artificial intelligence? npj Digital Medicine |
| title | Is regulatory science ready for artificial intelligence? |
| title_full | Is regulatory science ready for artificial intelligence? |
| title_fullStr | Is regulatory science ready for artificial intelligence? |
| title_full_unstemmed | Is regulatory science ready for artificial intelligence? |
| title_short | Is regulatory science ready for artificial intelligence? |
| title_sort | is regulatory science ready for artificial intelligence |
| url | https://doi.org/10.1038/s41746-025-01596-0 |
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