A policy framework for leveraging generative AI to address enduring challenges in clinical trials
Can artificial intelligence improve clinical trial design? Despite their importance in medicine, over 40% of trials involve flawed protocols. We introduce and propose the development of application-specific language models (ASLMs) for clinical trial design across three phases: ASLM development by re...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01440-5 |
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| _version_ | 1832594413091553280 |
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| author | Johnathon Edward Liddicoat Gabriela Lenarczyk Mateo Aboy Timo Minssen Sebastian Porsdam Mann |
| author_facet | Johnathon Edward Liddicoat Gabriela Lenarczyk Mateo Aboy Timo Minssen Sebastian Porsdam Mann |
| author_sort | Johnathon Edward Liddicoat |
| collection | DOAJ |
| description | Can artificial intelligence improve clinical trial design? Despite their importance in medicine, over 40% of trials involve flawed protocols. We introduce and propose the development of application-specific language models (ASLMs) for clinical trial design across three phases: ASLM development by regulatory agencies, customization by Health Technology Assessment bodies, and deployment to stakeholders. This strategy could enhance trial efficiency, inclusivity, and safety, leading to more representative, cost-effective clinical trials. |
| format | Article |
| id | doaj-art-6c43ee3502f64e1499ec23e38cdf9563 |
| institution | Kabale University |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-6c43ee3502f64e1499ec23e38cdf95632025-01-19T12:39:50ZengNature Portfolionpj Digital Medicine2398-63522025-01-01811510.1038/s41746-025-01440-5A policy framework for leveraging generative AI to address enduring challenges in clinical trialsJohnathon Edward Liddicoat0Gabriela Lenarczyk1Mateo Aboy2Timo Minssen3Sebastian Porsdam Mann4Dickson Poon School of Law, King’s College LondonCenter for Advanced Studies in Bioscience Innovation Law (CeBIL), Faculty of Law, University of CopenhagenCentre for Law, Medicine and Life Sciences and Centre for Intellectual Property & Information Law, Faculty of Law, University of CambridgeCenter for Advanced Studies in Bioscience Innovation Law (CeBIL), Faculty of Law, University of CopenhagenCenter for Advanced Studies in Bioscience Innovation Law (CeBIL), Faculty of Law, University of CopenhagenCan artificial intelligence improve clinical trial design? Despite their importance in medicine, over 40% of trials involve flawed protocols. We introduce and propose the development of application-specific language models (ASLMs) for clinical trial design across three phases: ASLM development by regulatory agencies, customization by Health Technology Assessment bodies, and deployment to stakeholders. This strategy could enhance trial efficiency, inclusivity, and safety, leading to more representative, cost-effective clinical trials.https://doi.org/10.1038/s41746-025-01440-5 |
| spellingShingle | Johnathon Edward Liddicoat Gabriela Lenarczyk Mateo Aboy Timo Minssen Sebastian Porsdam Mann A policy framework for leveraging generative AI to address enduring challenges in clinical trials npj Digital Medicine |
| title | A policy framework for leveraging generative AI to address enduring challenges in clinical trials |
| title_full | A policy framework for leveraging generative AI to address enduring challenges in clinical trials |
| title_fullStr | A policy framework for leveraging generative AI to address enduring challenges in clinical trials |
| title_full_unstemmed | A policy framework for leveraging generative AI to address enduring challenges in clinical trials |
| title_short | A policy framework for leveraging generative AI to address enduring challenges in clinical trials |
| title_sort | policy framework for leveraging generative ai to address enduring challenges in clinical trials |
| url | https://doi.org/10.1038/s41746-025-01440-5 |
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