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Assessment of Airway, Skin Color, and Use of Accessory Muscles

Assessment of Airway, Skin Color, and Use of Accessory Muscles

A thorough assessment of respiratory health is paramount in clinical settings to identify and manage respiratory distress and ensure adequate oxygenation. This article elaborates on the critical aspects of respiratory evaluation, including airway assessment, skin color examination, and the observation of accessory muscle use, which are integral to effectively diagnosing and managing patients with respiratory conditions.
Introduction
The initial evaluation of a patient's respiratory system...

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Large Language Models Outperform PGY-5 Residents on the Orthopaedic In-Training Examination: A Comparative Analysis

Rushil Dave¹, Nimit Vediya, Naman Sharma

¹From the Temerty Faculty of Medicine (Dave, Vediya, Sharma), University of Toronto, Toronto, ON, Canada, the Division of Orthopaedic Surgery (Shah, Whelan, Wolfstadt), Department of Surgery, University of Toronto, Toronto, ON, Canada, the Division of Orthopaedic Surgery (Whelan), Department of Surgery, St. Michael's Hospital, Unity Health Network, Toronto, ON, Canada, the University of Toronto Orthopaedic Sports Medicine (Whelan), Toronto, ON, Canada, and the Division of Orthopaedic Surgery (Wolfstadt), Department of Surgery, Mount Sinai Hospital, Toronto, ON, Canada.

The Journal of the American Academy of Orthopaedic Surgeons

|April 8, 2026

Summary

ChatGPT demonstrated superior performance on the 2024 Orthopaedic In-Training Examination (OITE), achieving postgraduate year five resident level. This highlights the potential of advanced large language models (LLMs) in medical education and orthopaedic practice.

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Area of Science:

Artificial Intelligence in Medicine
Medical Education Technology
Orthopaedic Surgery Assessment

Background:

Large language models (LLMs) show increasing use in medical education and assessments.
Previous LLMs performed at a first-year resident level on the 2022 Orthopaedic In-Training Examination (OITE).
Recent LLM advancements and image processing capabilities warrant further evaluation.

Purpose of the Study:

To evaluate the performance of six contemporary large language models (LLMs) on the 2024 Orthopaedic In-Training Examination (OITE).

Main Methods:

Six LLMs (ChatGPT, Gemini, Grok, Mistral, DeepSeek, Llama) were assessed on 203 multiple-choice questions.
Models capable of image and text processing were evaluated alongside text-only models.
Performance metrics included accuracy, image interpretation, and logical consistency, stratified by question difficulty.

Main Results:

ChatGPT achieved the highest accuracy (74.9%), logical consistency (72.4%), and image interpretation (73.8%).
Performance declined across all models as question difficulty increased.
Logical consistency strongly correlated with overall accuracy and correctness (P < 0.00001).

Conclusions:

ChatGPT demonstrated the highest performance, comparable to a postgraduate year five resident.
These findings suggest significant potential for LLM integration in orthopaedic clinical settings, electronic medical records, and surgical planning.
Future research should focus on improving performance on difficult questions and developing specialized orthopaedic LLMs.