Conversational AI in Tactical Casualty Care: Baseline GPT-4o Improves Combat Medic Decision-Making

Artificial Intelligence

artificial intelligence, combat medicine, decision-making, mechanical ventilation

The aim of the project is to investigate whether the integration of artificial intelligence (AI) support, specifically through the GPT-4 model, enhances the decision-making processes of military medical first responders within the framework of Tactical Combat Casualty Care (TCCC). The study focuses on AI's ability to assist in ventilator settings for injured individuals in combat scenarios, emphasizing improved accuracy and decision-making speed. The project tests the hypothesis that the use of AI can positively impact outcomes without compromising the autonomy of first responders. The results have the potential to optimize patient care in challenging conditions and contribute to the advancement of combat medicine.

This study investigates the potential of conversational artificial intelligence (AI), specifically GPT-4, to enhance clinical decision-making in Tactical Combat Casualty Care (TCCC) scenarios. The primary objective is to evaluate whether AI support improves the accuracy and efficiency of ventilator management decisions for combat medics in high-pressure environments without compromising their autonomy. A prospective, randomized, within-subject study design will be employed. Thirty combat medics from the Czech Armed Forces will participate. Each participant will complete 10 simulated TCCC scenarios: five with AI assistance and five without. Scenarios will be matched for complexity and randomized to control for order effects. Participants will use ChatGPT on handheld devices to simulate real-time AI-assisted decision-making. In scenarios involving AI assistance, medics will query GPT-4 for support in optimizing mechanical ventilator settings based on patient data, including blood gas results, vital signs, and ventilator parameters. The primary outcome is the accuracy of ventilator settings as categorized into excellent, acceptable, or failing based on predefined TCCC standards. Secondary outcomes include decision-making speed and participants' perception of AI's utility, measured through post-scenario surveys. The findings aim to determine the feasibility of integrating large language models (LLMs) into combat medical care to optimize patient outcomes and support medics under combat conditions. The study seeks to advance the understanding of AI's role in military medicine, providing a foundation for future deployment of fine-tuned AI solutions in TCCC and other critical care scenarios. This study offers a proof-of-concept evaluation of LLM applications in combat casualty care, with the potential to improve decision-making and inform the development of specialized AI tools for military use.

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Czechia