Artificial Intelligence in Triaging Patient Questions: An Evaluation of a Large Language Model for Distal Radius Fractures
KIMI generated responses that were generally safe, clinically concordant, and clearly communicated. These findings support the feasibility of deploying enhanced LLMs for asynchronous patient engagement in low-to-moderate risk care coordination settings.
Authors
- Alexander Lauder (ORCID: https://orcid.org/0000-0002-5889-0558)
- Samuel I. Berchuck (ORCID: https://orcid.org/0000-0001-5705-3144)
- Christine Shen (ORCID: https://orcid.org/0000-0003-4652-0899)
- Patricia K Wellborn
- Riley Kahan (ORCID: https://orcid.org/0009-0002-7777-4226)
- Christian A. Péan (ORCID: https://orcid.org/0000-0001-7445-1152)
- Hadi Javeed
- Andrew Federer
Institutions
- Medical Protective (US)
- Advisory Board Company (United States) (US)
- Duke University Health System (US)
Publication Details
- Journal
- Journal of the American Academy of Orthopaedic Surgeons
- Published
- 2025-08-27
- DOI
- https://doi.org/10.5435/jaaos-d-25-00456
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00