Breaking Boundaries in Spinal Surgery: GPT-4's Quest to Revolutionize Surgical Site Infection Management

Abstract

BACKGROUND: Surgical site infection (SSI) is a common and costly complication in spinal surgery. Identifying risk factors and preventive strategies is crucial for reducing SSIs. Generative Pre-trained Transformer 4 (GPT-4) has evolved from a simple text-based tool to a sophisticated multimodal data expert, invaluable for clinicians. This study explored GPT-4's applications in SSI management across various clinical scenarios. METHODS: GPT-4 was employed in clinical scenarios related to SSIs in spinal surgery. Researchers designed specific questions for GPT-4 to generate tailored responses. Six evaluators assessed the responses for logic and accuracy using a 5-point Likert scale. Interrater consistency was measured with Fleiss' kappa, and radar charts visualized GPT-4's performance. RESULTS: Interrater consistency, measured by Fleiss' kappa, ranged from 0.62 to 0.83. The average scores for logic and accuracy were 24.27 ± 0.4 and 24.46 ± 0.25. Radar charts indicated consistently high performance across criteria. GPT-4 demonstrated proficiency in creating personalized treatment plans, improving SSI management strategies, and identified emerging research trends. CONCLUSIONS: GPT-4 shows a significant potential in SSI management in spinal surgery, promoting patient-centered care and precision medicine. Despite limitations in antibiotics and patient education, GPT-4's continuous learning, data privacy focus, and professional collaboration indicate its potential to revolutionize SSI management, requiring further development.

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