Clinical Report: Utilization of NLP and LLMs in Sports Injury Evaluation

Overview

This scoping review evaluates the application of natural language processing (NLP) and large language models (LLMs) in sports injury assessment and rehabilitation decision-making. It identifies key findings, challenges, and knowledge gaps in the current research landscape.

Background

The assessment of sports injuries and rehabilitation decisions often relies on quantitative data, which may not fully capture the complexities involved. Unstructured text data in electronic health records and clinical notes can provide critical insights into injury mechanisms and patient readiness. The integration of NLP and LLMs into this field presents an opportunity to enhance the evaluation process and improve clinical outcomes.

Data Highlights

Key Findings

• 27 studies were included in the review, primarily from the United States.
• The main research types were algorithm evaluation and benchmarking.
• Healthcare professionals were the primary target population for these studies.
• Challenges included readability issues and AI hallucinations.
• The overall compliance rate with MINIMAR reporting standards was 61.70%.
• Future research should focus on athlete-centered datasets and external validation.

Clinical Implications

Healthcare professionals should be aware of the potential of NLP and LLMs in sports injury assessment while recognizing their current limitations. Addressing issues such as readability and methodological reporting will be essential for effective clinical application.

Conclusion

LLMs hold promise for enhancing sports injury assessment and rehabilitation decision-making, but significant challenges remain that must be addressed to improve their reliability and applicability in clinical settings.

Related Resources & Content

1. Frontiers | Applications of Natural Language Processing and Large Language Models in Sports Injury Assessment and Rehabilitation Decision-Making: A Scoping Review
2. DIGITAL HEALTH — From injury to comeback: A systematic review of machine learning models predicting return to sport in athletes
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9. Artificial intelligence and machine learning in sports medicine: mapping clinical tasks and assessing clinical maturity - a scoping review | BMC Medical Informatics and Decision Making | Springer Nature Link
10. Frontiers | Applications of Natural Language Processing and Large Language Models in Sports Injury Assessment and Rehabilitation Decision-Making: A Scoping Review
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