Comparison of AI-Based Triage Systems in High-Energy Thoracic Trauma: A Pilot Study Using ChatGPT and Gemini

Sürecinde ChatGPT-4 ve Gemini 1.5'in Karşılaştırmalı Performansı: 30 Sentetik Olguda Simülasyon Çalışması Background: High-energy thoracic trauma is a leading cause of morbidity and mortality in polytrauma patients.Timely diagnosis and management are crucial for patient outcomes.Artificial intelligence (AI) systems such as ChatGPT and Gemini have demonstrated potential in providing clinical decision support.This study presents a dataset of 30 synthetic thoracic trauma cases and explores the feasibility of using AI tools to assist in triage and early management decisions. Materials and Methods:Thirty synthetic cases, reflecting real-world patterns of high-energy thoracic trauma, were developed based on established trauma mechanisms, clinical findings, and evidence-based physiological parameters.Each case includes vital signs, injury mechanism, physical findings, and a structured clinical question regarding immediate management.These cases were formatted for AI analysis.Responses from ChatGPT and Gemini will be analyzed for clinical appropriateness, adherence to trauma guidelines (e.g., Advanced Trauma Life Support), and concordance with experts.Results: Initial simulations (5-case pilot) demonstrated greater adherence to trauma protocols with ChatGPT, particularly in managing tension pneumothorax, tamponade, and flail chest.Gemini responses were more conservative and occasionally delayed critical interventions.A full-scale analysis of all 30 cases is underway, using a scoring system based on accuracy, guideline conformity, and intervention prioritization.Preliminary findings suggest AI tools can assist in high-stakes clinical decision-making, particularly in environments with limited specialist access.ChatGPT's ability to provide structured, guideline-based responses is promising.However, human oversight remains essential.Large-scale validation with real patient data is necessary before clinical deployment.Conclusion: AI-based models show potential in enhancing early decision-making in high-energy thoracic trauma.While encouraging, these tools require careful integration into clinical workflows and further validation in real-time settings.