Comparative Analysis of Large Language Models and Spine Surgeons in Surgical Decision-Making and Radiological Assessment for Spine Pathologies
The study highlights the potential of LLMs in assisting with radiological interpretation and surgical decision-making in spine surgery. However, the current limitations, such as the lack of consideration for patient-specific factors and inaccuracies in treatment recommendations, emphasize the need for further refinement and validation of these artificial intelligence (AI) models. Continued collaboration between AI researchers and clinical experts is crucial to address these challenges and realize the full potential of AI in spine surgery.
Authors
- Salah G. Aoun (ORCID: https://orcid.org/0000-0003-3499-7569)
- James P. Caruso (ORCID: https://orcid.org/0000-0002-5562-6436)
- Angela Hawkins
- Soummitra Anand (ORCID: https://orcid.org/0000-0002-9869-4957)
- Carlos A. Bagley (ORCID: https://orcid.org/0000-0003-2041-5273)
- Ahmad Kareem Almekkawi (ORCID: https://orcid.org/0000-0003-3470-5191)
- Rayaan Rauf
- Mayar Al-Shaikhli
Institutions
- Saint Luke's Hospital (US)
- University of Missouri–Kansas City (US)
- The University of Texas Southwestern Medical Center (US)
Publication Details
- Journal
- World Neurosurgery
- Published
- 2024-12-24
- DOI
- https://doi.org/10.1016/j.wneu.2024.11.114
- Citations
- 8
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.96