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Enhancing systematic review efficiency in hand surgery using artificial intelligence (natural language processing)
Gordon C Wong1, Robert L Kane2, Cheng-C J Chu3
1Section of Plastic Surgery, Department of Surgery, University of Michigan Medical School, Ann Arbor, MI, USA.
The Journal of Hand Surgery, European Volume
|November 15, 2024
Summary
This study trained a natural language processing (NLP) model to improve hand surgery systematic review screening. The NLP model accelerated abstract screening by 31% without reducing accuracy.
Area of Science:
- Orthopedic Surgery
- Medical Informatics
- Natural Language Processing
Background:
- Systematic reviews are crucial for evidence-based medicine but are time-consuming.
- Abstract screening is a major bottleneck in systematic reviews.
- Automating screening can enhance efficiency and reduce reviewer burden.
Purpose of the Study:
- To develop and evaluate a natural language processing (NLP) model for screening hand surgery research abstracts.
- To assess the efficiency gains and accuracy of using NLP in systematic review screening.
- To identify key text elements in abstracts related to hand surgery interventions and outcomes.
Main Methods:
- Annotated 1600 hand surgery abstracts for NLP model training.
- Trained an NLP model to recognize key text elements in research abstracts.
- Conducted two experiments processing 200 abstracts with NLP assistance for inclusion/exclusion decisions.
Main Results:
- The NLP model achieved 0.91 accuracy in recognizing key text elements, particularly study interventions.
- Mean abstract screening time was reduced by 31% using the NLP model.
- Model precision improved in a second experiment, suggesting context-dependent performance.
Conclusions:
- NLP models can significantly streamline abstract screening for systematic reviews in hand surgery.
- This approach enhances efficiency by accurately identifying original research and relevant text.
- NLP tools offer a promising solution to accelerate evidence synthesis in orthopedic research.
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