Abstract
Introduction
As artificial intelligence (AI) becomes increasingly integrated into medicine and surgery, its applications are expanding rapidly—from aiding clinical documentation to providing patient information. However, its role in medical decision-making remains uncertain. This study evaluates an AI language model’s alignment with clinical consensus statements in foot and ankle surgery.
Methods
Clinical consensus statements from the American College of Foot and Ankle Surgeons (ACFAS; 2015-2022) were collected and rated by ChatGPT-o1 as being inappropriate, neither appropriate nor inappropriate, and appropriate. Ten repetitions of the statements were entered into ChatGPT-o1 in a random order, and the model was prompted to assign a corresponding rating. The AI-generated scores were compared to the expert panel’s ratings, and intra-rater analysis was performed.
Results
The analysis of 9 clinical consensus documents and 129 statements revealed an overall Cohen’s kappa of 0.29 (95% CI: 0.12, 0.46), indicating fair alignment between expert panelists and ChatGPT. Overall, ankle arthritis and heel pain showed the highest concordance at 100%, while flatfoot exhibited the lowest agreement at 25%, reflecting variability between ChatGPT and expert panelists. Among the ChatGPT ratings, Cohen’s kappa values ranged from 0.41 to 0.92, highlighting variability in internal reliability across topics.
Conclusion
ChatGPT achieved overall fair agreement and demonstrated variable consistency when repetitively rating ACFAS expert panel clinical practice guidelines representing a variety of topics. These data reflect the need for further study of the causes, impacts, and solutions for this disparity between intelligence and human intelligence.
Level of Evidence:
Level IV: Retrospective cohort study
Keywords
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