The Artist versus the Machine: Evaluating ChatGPT Efficacy in Antimicrobial Management for Pediatric Traumatic Wounds

To evaluate the efficacy of Chat Generative Pre-Trained Transformer (ChatGPT) in generating antimicrobial management recommendations for pediatric patients with contaminated traumatic wounds, particularly in the absence of infectious disease (ID) consultation.

Three pediatric cases involving severely contaminated traumatic injuries were retrospectively presented to ChatGPT-4, including clinical data such as injury mechanism, cultures, susceptibilities, and presence of osteomyelitis. The model's antibiotic recommendations were compared with those made by the pediatric ID team. A pediatric ID specialist graded each recommendation based on appropriateness, pathogen coverage, and clinical judgment.

ChatGPT provided 29 antibiotic recommendations: 3 for initial antibiotic selection, 8 for pathogen management, and 18 for follow-up changes. All of the initial antibiotic recommendations were deemed appropriate or identical to ID guidance. Of the pathogen-specific recommendations, 87.5% were appropriate and 12.5% were unnecessary. Among the follow-up changes, 11.1% were appropriate, 77.8% were unnecessary, and 11.1% were incorrect. In total, 41.4% of recommendations were appropriate or identical, 51.7% were unnecessary, and 6.9% were incorrect. The majority of unnecessary changes involved redundant adjustments to existing effective therapies.

ChatGPT performed well in initial antibiotic selection and pathogen-directed management but frequently proposed unnecessary follow-up modifications. Although promising as a supplemental tool in resource-limited settings, its current limitations highlight the need for further refinement before broader clinical application.