SO17. From Months To Minutes: Evaluating an AI-driven Approach to Systematic Reviews in Plastic Surgery

Abstract

PURPOSE: Systematic reviews are essential for evidence-based plastic surgery but hindered by manual article screening. We developed an artificial intelligence (AI) application to automate systematic review stages to accelerate evidence synthesis while maintaining accuracy. METHODS: We created a multi-agent natural language processing workflow using GPT-3.5 and GPT-4 for article screening. Testing involved four published systematic reviews in plastic surgery, covering digital replantation to facial nerve grading systems. AI workflow performance was compared to human-conducted reviews at title/abstract and full-text screening stages. RESULTS: The AI processed 5,769 studies across four systematic reviews, completing screening in 1.5-13.1 minutes per review based on complexity. Title/abstract screening achieved 98.4% precision (95% CI: 97.8-98.9%) and 92.5% recall (95% CI: 91.6-93.3%). Full-text evaluation reached 99.3% precision (95% CI: 98.9-99.6%) and 98.6% recall (95% CI: 98.1-99.0%). The AI excelled in identifying quantitative outcomes but showed limitations in nuanced clinical evaluations. Average computational cost was $18.66 per review, significantly below manual review costs. CONCLUSION: This AI approach transforms reviews in plastic surgery, reducing time and cost while maintaining accuracy and expediting research implementation in clinical practice. Future development will focus on creating a user-friendly interface for broader accessibility. AI integration could revolutionize evidence-based plastic surgery through frequent updates, enhancing clinical decision-making.

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