Assessing the Accuracy of Artificial Intelligence-Generated Clinical Summaries From Ambulatory Glaucoma Subspecialty Clinical Encounters
LLMs may enhance patient experience and health literacy by standardizing patient-friendly language in clinical care.
Authors
- Daniel L. Liebman (ORCID: https://orcid.org/0000-0002-2491-7572)
- Michael V. Boland (ORCID: https://orcid.org/0000-0003-2506-7095)
- Steven R. Hahn
- In Young Chung (ORCID: https://orcid.org/0000-0003-0671-244X)
- Yapei Zhang (ORCID: https://orcid.org/0000-0001-7427-8775)
- Lan Shen (ORCID: https://orcid.org/0000-0002-8332-2480)
- Min Shi
- Mengyu Wang
- Louis R. Pasquale
- Laura E. Barna
Institutions
- Massachusetts Eye and Ear Infirmary (US)
- Harvard University (US)
- Smith-Kettlewell Eye Research Institute (US)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- Translational Vision Science & Technology
- Published
- 2026-01-14
- DOI
- https://doi.org/10.1167/tvst.15.1.22
- Citations
- 1
- Primary Topic
- Electronic Health Records Systems
- Type
- article
- Field-Weighted Citation Impact
- 195.93
Funders
- Research to Prevent Blindness
- Glaucoma Foundation
- Carl Zeiss Meditec AG
- National Institutes of Health
- Genentech