Can Large Language Models (LLMs) Predict the Appropriate Treatment of Acute Hip Fractures in Older Adults? Comparing Appropriate Use Criteria With Recommendations From ChatGPT
ChatGPT-4.0 scores were not concordant with AAOS scores, overestimating the appropriateness of total hip arthroplasty, hemiarthroplasty, and long cephalomedullary nails, and underestimating the other three. ChatGPT-4.0 was inadequate in selecting an appropriate treatment deemed acceptable, most reasonable, and most likely to improve patient outcomes.
Authors
- Katrina S. Nietsch (ORCID: https://orcid.org/0009-0007-1605-8193)
- Krystin A. Hidden (ORCID: https://orcid.org/0000-0002-1107-7391)
- Renee Ren (ORCID: https://orcid.org/0000-0002-2740-940X)
- Nancy Shrestha (ORCID: https://orcid.org/0000-0003-0314-7748)
- Bashar Zaidat
- Samuel Kang-Wook Cho (ORCID: https://orcid.org/0000-0001-7511-2486)
- Akiro H. Duey (ORCID: https://orcid.org/0000-0003-0308-0512)
- Laura C.M. Ndjonko (ORCID: https://orcid.org/0000-0002-3153-4967)
- Jun Kim (ORCID: https://orcid.org/0000-0002-6114-2673)
- Mateo Restrepo Mejia (ORCID: https://orcid.org/0009-0003-0457-3308)
- Wasil Ahmed
- Samuel Q Li
Institutions
- Rosalind Franklin University of Medicine and Science (US)
Publication Details
- Journal
- JAAOS Global Research and Reviews
- Published
- 2024-08-01
- DOI
- https://doi.org/10.5435/jaaosglobal-d-24-00206
- Citations
- 10
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 1.20