Battle of the Bots: Solving Clinical Cases in Osteoarticular Infections With Large Language Models
OpenEvidence and Miccrosoft Copilot achieved the highest accuracy among evaluated LLMs, highlighting their potential for precisely addressing complex clinical cases. This study emphasizes the need for specialized, validated artificial intelligence tools in medical practice. Although promising, current models face limitations in real-world applications, requiring further refinement to support clinical decision making reliably.
Authors
- Takahiro Matsuo (ORCID: https://orcid.org/0000-0001-9389-0628)
- Francesco Petri (ORCID: https://orcid.org/0000-0003-0125-9551)
- Fabio Borgonovo (ORCID: https://orcid.org/0000-0001-5796-671X)
- Andrea Gori (ORCID: https://orcid.org/0000-0001-6587-4794)
- Elie F. Berbari (ORCID: https://orcid.org/0000-0001-9817-5391)
- Seyed Mohammad Amin Alavi (ORCID: https://orcid.org/0000-0001-7885-6691)
- Laura C.M. Ndjonko (ORCID: https://orcid.org/0000-0002-3153-4967)
Institutions
- Northwestern University (US)
- Ahvaz Jundishapur University of Medical Sciences (IR)
- Mayo Clinic (US)
- University of Milan (IT)
- Luigi Sacco Hospital (IT)
- Mayo Clinic in Arizona (US)
- Mayo Clinic in Florida (US)
Publication Details
- Journal
- Mayo Clinic Proceedings Digital Health
- Published
- 2025-05-23
- DOI
- https://doi.org/10.1016/j.mcpdig.2025.100230
- Citations
- 6
- Primary Topic
- Orthopedic Infections and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 12.04
Funders
- National Institutes of Health
- National Center for Advancing Translational Sciences