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

NCT06796036 | Completed

• 1 other identifier: FieldAI
• Study Type: interventional
• Target: 42
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Artificial Intelligence

Keywords

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

Outcome Measures

Primary Outcomes (1)

• Accuracy of ventilator settings

  Accuracy of ventilator settings as categorized into "excellent," "acceptable," or "failing" based on predefined TCCC standards. Excellent means 2 points, acceptable 1 point and failing 0 point.

  1 hour

Other Outcomes (1)

• Perception of artificial intelligence's utility

  1 hour

Study Arms (1)

Combat Medic Decision-Making with and without AI Assistance

EXPERIMENTAL

All participants will complete 10 Tactical Combat Casualty Care scenarios: 5 with AI assistance using GPT-4 for ventilator management and 5 without AI assistance. The crossover design ensures each participant experiences both conditions.

Other: Combat Medic Decision-Making with and without artificial intelligence assistance

Interventions

Combat Medic Decision-Making with and without artificial intelligence assistance OTHER

Participants will complete 10 simulated Tactical Combat Casualty Care (TCCC) scenarios, with 5 scenarios conducted using AI assistance (GPT-4) and 5 without AI. In AI-assisted scenarios, participants will use GPT-4 to query and optimize ventilator settings based on patient data, while non-AI scenarios rely solely on their clinical judgment.

Combat Medic Decision-Making with and without AI Assistance

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Combat medics actively serving in the Czech Armed Forces
• Completion of standardized Tactical Combat Casualty Care training modules and e-learning on ventilator settings and blood gas interpretation
• Successful passing of pre-tests to ensure a uniform baseline knowledge level.
• Willingness to participate and provide informed consent.
• Availability to complete the full study protocol, including 10 simulated scenarios.

You may not qualify if:

• Failure to pass the pre-tests or complete TCCC and ventilator management training
• Prior advanced training or professional certification in critical care or mechanical ventilation that could bias results
• Refusal to provide informed consent or inability to commit to the study schedule

Sponsors & Collaborators

• Charles University, Czech Republic: lead
• Czech Technical University in Prague: collaborator

Study Sites (1)

Military University Hospital Prague

Prague, 16209, Czechia

Location

Related Publications (2)

• Nemeth C, Amos-Binks A, Rule G, Laufersweiler D, Keeney N, Flint I, Pinevich Y, Herasevich V. TCCC Decision Support With Machine Learning Prediction of Hemorrhage Risk, Shock Probability. Mil Med. 2023 Nov 8;188(Suppl 6):659-665. doi: 10.1093/milmed/usad298.

  PMID: 37948287 RESULT

• Preiksaitis C, Ashenburg N, Bunney G, Chu A, Kabeer R, Riley F, Ribeira R, Rose C. The Role of Large Language Models in Transforming Emergency Medicine: Scoping Review. JMIR Med Inform. 2024 May 10;12:e53787. doi: 10.2196/53787.

  PMID: 38728687 RESULT

Study Officials

• Michal Soták, M.D., Ph.D.

  Charles University, Czech Republic

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: OTHER
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Model Details: Crossover assignment (each participant acts as their own control in scenarios with and without artificial intelligence)

Study Record Dates

• First Submitted: January 22, 2025
• First Posted: January 28, 2025
• Study Start: February 2, 2025
• Primary Completion: March 31, 2025
• Study Completion: April 30, 2025
• Last Updated: May 13, 2025

Record last verified: 2025-05

Data Sharing

• IPD Sharing: Will not share

Locations

CZ (1)