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Assessing the Third Wave of Generative AI: Performance of Advanced Models on Text-based Questions From the 2024

Daniel S Hayes¹, Alyssa Barré, Ryan D Muchow

¹From the University of Kentucky College of Medicine (Hayes), and the University of Kentucky Department of Orthopaedic Surgery and Sports Medicine, Lexington, KY (Barré and Muchow).

The Journal of the American Academy of Orthopaedic Surgeons

|December 29, 2025

Summary

This summary is machine-generated.

Generative artificial intelligence (AI) models show high performance on the Orthopaedic In-training Examination, with most exceeding the passing standard and outperforming senior residents. These AI tools offer potential as supplemental educational resources in orthopaedics.

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Published on: June 13, 2025

Area of Science:

Medical Education Technology
Artificial Intelligence in Medicine
Orthopaedic Surgery Training

Background:

Generative artificial intelligence (AI) is increasingly adopted by learners in educational settings.
The performance of AI models in specialized medical examinations is not well-established.
Assessing AI capabilities is crucial for understanding their role in orthopaedic education.

Purpose of the Study:

To evaluate the performance of recent generative AI models on the 2024 American Academy of Orthopaedic Surgeons Orthopaedic In-training Examination.
To determine the potential applications of these AI models in orthopaedic education.
To compare AI model performance with orthopaedic resident performance.

Main Methods:

Eleven advanced AI models were tested using 119 verbatim questions from the 2024 Orthopaedic In-training Examination.
Model outputs were assessed for correctness, reasoning time, citation accuracy, and confidence.
Performance was compared against the examination's minimal passing standard and orthopaedic resident (PGY5) performance.

Main Results:

Ten of eleven AI models surpassed the minimal passing standard (67.7%).
Eight AI models outperformed the mean performance of PGY5 residents.
OpenAI's o1Pro with Deep Research achieved the highest accuracy (90.8%), exceeding PGY5 residents by 17.8%.

Conclusions:

Advanced AI models demonstrate significant proficiency on orthopaedic in-training exams, with many exceeding senior resident performance.
AI models show promise as supplemental educational tools in orthopaedics, but limitations in clinical reasoning and imaging interpretation persist.
Further research is needed to optimize the integration of AI into orthopaedic training programs.