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Exploring the knowledge base of ChatGPT in lateral elbow tendinopathy
J Shepherd1,2,3, T Talavia1, P Raval1
1Trauma & Orthopaedic Surgery Department, Leicester Royal Infirmary, University Hospitals of Leicester NHS Trust, Leicester, UK.
Large language models show promise in identifying diagnoses for conditions like lateral elbow tendinopathy (LET). However, current limitations in evidence accuracy and access prevent their justified clinical application.
Area of Science:
- Artificial Intelligence in Healthcare
- Clinical Decision Support Systems
Background:
- Large language models (LLMs) offer potential for improving healthcare triage.
- LLMs can process and generate human-like text for various applications.
- Evaluating LLM application in clinical settings is crucial.
Purpose of the Study:
- To assess the capabilities of ChatGPT-3.5 in diagnosing and managing lateral elbow tendinopathy (LET).
- To evaluate the quality and accuracy of evidence provided by LLMs for clinical scenarios.
Main Methods:
- ChatGPT-3.5 was queried on LET investigation and management, providing evidence sources.
- Five clinical scenarios with typical and red-flag features were presented to the model.
- Responses were assessed by three upper-limb consultants using the DISCERN tool.
Main Results:
- Moderate quality ratings for both question and scenario responses.
- Accurate identification of diagnoses and red-flag features.
- 40% of cited references were non-existent; others contained errors or outdated information.
Conclusions:
- LLM's diagnostic and red-flag identification abilities show potential.
- Clinical application is not yet justified due to evidence basis limitations.
- Lack of real-time evidence access hinders current clinical use.
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