Billing and Coding in Foot and Ankle Surgery: Can We Trust Artificial Intelligence?

BackgroundBilling and coding for orthopaedic procedures is a complex process with thousands of procedure codes and associated modifiers in existence. Foot and ankle faces an additional challenge as it is among the highest variability regarding procedures performed compared with other orthopaedic subspecialities. This study aimed to investigate the capabilities of the top AI search engines in accurately identifying Current Procedural Terminology (CPT) codes for common foot and ankle procedures.MethodsA comparative analysis of 3 publically available AI search engines (ChatGPT, Bing, and Google Gemini) was performed investigating their accuracy in generating CPT codes for common orthopaedic foot and ankle procedures. The generated CPT codes were recorded and compared with the codes generated by 3 fellowship trained foot and ankle surgeons, serving as the reference standard. Cohen kappa coefficient was used to determine agreement across AI platforms regarding the surgeon coding reference standard. ResultsThe AI search engines were able to correctly generate the appropriate CPT codes 44% of the time, with Bing being the most accurate in generating the correct CPT codes for 8 of the 13 procedures (62%) and partially correct codes 3 of the 13 procedures (23%). ChatGPT demonstrated the worst accuracy, generating the correct CPT codes only 23% of the time (3/13). AI platforms demonstrated an overall Fair Agreement with the reference standard (kappa = 0.201). Individually, Bing demonstrated Moderate Agreement (kappa = 0.405), Google Gemini demonstrated Fair Agreement (kappa = 0.255), and ChatGPT demonstrated Poor Agreement with the reference standard (kappa = 0.171).ConclusionAlthough the capabilities of AI show great promise for many industries, the results of this study bring caution to relying on AI for accurately generating orthopaedic foot and ankle procedure CPT codes.Level of Evidence:<i>III, Comparative Study</i>.

Authors

Institutions

Publication Details

Journal
Foot & Ankle Specialist
Published
2025-08-20
DOI
https://doi.org/10.1177/19386400251363000
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Billing and Coding in Foot and Ankle Surgery: Can We Trust Artificial Intelligence?

Selene G. Parekh, Adam Lencer, David I. Pedowitz, Joseph A.S. McCahon et al.
Foot & Ankle Specialist
Artificial Intelligence in Healthcare and Education
article

Billing and Coding in Foot and Ankle Surgery: Can We Trust Artificial Intelligence?

Selene G. Parekh, Adam Lencer, David I. Pedowitz, Joseph A.S. McCahon, Joseph N. Daniel, Amy Nghe, Nana Amponsah, Gina Provenzano
article en

Abstract

BackgroundBilling and coding for orthopaedic procedures is a complex process with thousands of procedure codes and associated modifiers in existence. Foot and ankle faces an additional challenge as it is among the highest variability regarding procedures performed compared with other orthopaedic subspecialities. This study aimed to investigate the capabilities of the top AI search engines in accurately identifying Current Procedural Terminology (CPT) codes for common foot and ankle procedures.MethodsA comparative analysis of 3 publically available AI search engines (ChatGPT, Bing, and Google Gemini) was performed investigating their accuracy in generating CPT codes for common orthopaedic foot and ankle procedures. The generated CPT codes were recorded and compared with the codes generated by 3 fellowship trained foot and ankle surgeons, serving as the reference standard. Cohen kappa coefficient was used to determine agreement across AI platforms regarding the surgeon coding reference standard. ResultsThe AI search engines were able to correctly generate the appropriate CPT codes 44% of the time, with Bing being the most accurate in generating the correct CPT codes for 8 of the 13 procedures (62%) and partially correct codes 3 of the 13 procedures (23%). ChatGPT demonstrated the worst accuracy, generating the correct CPT codes only 23% of the time (3/13). AI platforms demonstrated an overall Fair Agreement with the reference standard (kappa = 0.201). Individually, Bing demonstrated Moderate Agreement (kappa = 0.405), Google Gemini demonstrated Fair Agreement (kappa = 0.255), and ChatGPT demonstrated Poor Agreement with the reference standard (kappa = 0.171).ConclusionAlthough the capabilities of AI show great promise for many industries, the results of this study bring caution to relying on AI for accurately generating orthopaedic foot and ankle procedure CPT codes.Level of Evidence:<i>III, Comparative Study</i>.

Foot & Ankle Specialist
Thomas Jefferson University Hospital (US), Rothman Orthopaedics (US)
No poverty
Openalex Percentile: Top 85%
Artificial Intelligence in Healthcare and Education
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.