Clinician Perspectives on AI-Generated Drafts of Patient Test Result Explanations
This quality improvement study evaluates clinician perspectives on the usability and utility of generative artificial intelligence (AI)–based large language model tool to draft result comments for laboratory, imaging, and pathology results.
Authors
- Kirsten Murtagh
- Arun Lakhotia (ORCID: https://orcid.org/0000-0001-9943-7795)
- A. Bhasin (ORCID: https://orcid.org/0000-0002-3687-8179)
- Christopher Sharp (ORCID: https://orcid.org/0000-0001-7169-6904)
- P. Stephen (ORCID: https://orcid.org/0000-0003-3738-9569)
- Laurna Bullock (ORCID: https://orcid.org/0000-0002-4193-1835)
- Steven Lin (ORCID: https://orcid.org/0000-0002-7542-4663)
- Michael A. Pfeffer (ORCID: https://orcid.org/0000-0002-8958-0843)
- Abishek Nair
- Shreya Shah (ORCID: https://orcid.org/0000-0002-8242-7889)
- Danyelle Clutter
- Patricia García (ORCID: https://orcid.org/0000-0002-9058-9933)
- Margaret Smith (ORCID: https://orcid.org/0009-0002-3874-4542)
- H. Schmidt (ORCID: https://orcid.org/0009-0000-1058-1177)
- Kyle Vogt
- Liban Sheikh
Institutions
- Stanford Health Care (US)
- Stanford Medicine (US)
- Stanford University (US)
Publication Details
- Journal
- JAMA Network Open
- Published
- 2025-08-22
- DOI
- https://doi.org/10.1001/jamanetworkopen.2025.28794
- Citations
- 5
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 3.27