Perspectives on Artificial Intelligence–Generated Responses to Patient Messages
This cross-sectional study of patient queries in US electronic health records examines laypersons’ satisfaction with answers generated with artificial intelligence (AI) compared with clinician responses, and whether results were concordant with clinician-determined quality of AI responses.
Authors
- Michael L. Chen (ORCID: https://orcid.org/0000-0002-0168-1122)
- Christopher Sharp (ORCID: https://orcid.org/0000-0001-7169-6904)
- Natalie M. Pageler (ORCID: https://orcid.org/0000-0002-5853-289X)
- Eleni Linos
- Michael A. Pfeffer (ORCID: https://orcid.org/0000-0002-8958-0843)
- Shawheen J. Rezaei (ORCID: https://orcid.org/0000-0002-4394-7371)
- Steven M. Asch (ORCID: https://orcid.org/0000-0001-5838-5335)
- Jiyeong Kim (ORCID: https://orcid.org/0000-0002-2869-5751)
- Sonia Onyeka
- Rayhan A. Lal
- April S. Liang (ORCID: https://orcid.org/0009-0002-3890-4104)
- Susan M. Seav
- Shivam Vedak
- David Mui
- Julie Lee (ORCID: https://orcid.org/0009-0002-6640-8597)
Institutions
- Stanford Health Care (US)
- Digital Science (United States) (US)
- Stanford Medicine (US)
- Stanford University (US)
Publication Details
- Journal
- JAMA Network Open
- Published
- 2024-10-16
- DOI
- https://doi.org/10.1001/jamanetworkopen.2024.38535
- Citations
- 27
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 3.25