Evaluating the Efficacy of Large Language Models in CPT Coding for Craniofacial Surgery: A Comparative Analysis
This study highlights the feasibility and potential benefits of integrating LLMs into the CPT coding process for craniofacial surgery. The findings advocate for further refinement and training of AI models to improve their accuracy and practicality, suggesting a future where AI-assisted coding could become a standard component of surgical workflows, aligning with the ongoing digital transformation in health care.
Authors
- Angeleah Carreras (ORCID: https://orcid.org/0000-0003-3758-3994)
- D. Mitchell Self
- Theodore E. Habarth-Morales (ORCID: https://orcid.org/0000-0002-3662-5294)
- Mario A. Aycart (ORCID: https://orcid.org/0000-0001-7189-0969)
- Abhijeet Sambangi
- Advith Sarikonda (ORCID: https://orcid.org/0009-0002-7138-4331)
- Adrija Sircar
- EJ Caterson
- Emily L. Isch (ORCID: https://orcid.org/0009-0002-1974-6972)
Institutions
- Thomas Jefferson University (US)
- Jefferson Hospital for Neuroscience (US)
Publication Details
- Journal
- Journal of Craniofacial Surgery
- Published
- 2024-09-02
- DOI
- https://doi.org/10.1097/scs.0000000000010575
- Citations
- 11
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 1.32