Preventing unrestricted and unmonitored AI experimentation in healthcare through transparency and accountability
Abstract The integration of large language models (LLMs) into electronic health records offers potential benefits but raises significant ethical, legal, and operational concerns, including unconsented data use, lack of governance, and AI-related malpractice accountability. Sycophancy, feedback loop...
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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/s41746-025-01443-2 |
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| author | Donnella S. Comeau Danielle S. Bitterman Leo Anthony Celi |
| author_facet | Donnella S. Comeau Danielle S. Bitterman Leo Anthony Celi |
| author_sort | Donnella S. Comeau |
| collection | DOAJ |
| description | Abstract The integration of large language models (LLMs) into electronic health records offers potential benefits but raises significant ethical, legal, and operational concerns, including unconsented data use, lack of governance, and AI-related malpractice accountability. Sycophancy, feedback loop bias, and data reuse risk amplifying errors without proper oversight. To safeguard patients, especially the vulnerable, clinicians must advocate for patient-centered education, ethical practices, and robust oversight to prevent harm. |
| format | Article |
| id | doaj-art-c21757c45371490b9ece570b0464e232 |
| 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-c21757c45371490b9ece570b0464e2322025-01-19T12:39:41ZengNature Portfolionpj Digital Medicine2398-63522025-01-01811710.1038/s41746-025-01443-2Preventing unrestricted and unmonitored AI experimentation in healthcare through transparency and accountabilityDonnella S. Comeau0Danielle S. Bitterman1Leo Anthony Celi2Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical SchoolDepartment of Radiation Oncology, Brigham and Women’s Hospital/Dana-Farber Cancer Institute, Harvard Medical SchoolLaboratory for Computational Physiology, Massachusetts Institute of TechnologyAbstract The integration of large language models (LLMs) into electronic health records offers potential benefits but raises significant ethical, legal, and operational concerns, including unconsented data use, lack of governance, and AI-related malpractice accountability. Sycophancy, feedback loop bias, and data reuse risk amplifying errors without proper oversight. To safeguard patients, especially the vulnerable, clinicians must advocate for patient-centered education, ethical practices, and robust oversight to prevent harm.https://doi.org/10.1038/s41746-025-01443-2 |
| spellingShingle | Donnella S. Comeau Danielle S. Bitterman Leo Anthony Celi Preventing unrestricted and unmonitored AI experimentation in healthcare through transparency and accountability npj Digital Medicine |
| title | Preventing unrestricted and unmonitored AI experimentation in healthcare through transparency and accountability |
| title_full | Preventing unrestricted and unmonitored AI experimentation in healthcare through transparency and accountability |
| title_fullStr | Preventing unrestricted and unmonitored AI experimentation in healthcare through transparency and accountability |
| title_full_unstemmed | Preventing unrestricted and unmonitored AI experimentation in healthcare through transparency and accountability |
| title_short | Preventing unrestricted and unmonitored AI experimentation in healthcare through transparency and accountability |
| title_sort | preventing unrestricted and unmonitored ai experimentation in healthcare through transparency and accountability |
| url | https://doi.org/10.1038/s41746-025-01443-2 |
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