Artificial IntelligenceFieldAINCT06796036
Conversational AI in Tactical Casualty Care: Baseline GPT-4o Improves Combat Medic Decision-Making
A study testing Combat Medic Decision-Making with and without artificial intelligence assistance in people with Artificial Intelligence, run by Charles University, Czech Republic at 1 site in Czechia.
Trial 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). Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Artificial Intelligence
- What is being tested?
- Combat Medic Decision-Making with and without artificial intelligence assistance
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 42 participants
- Where
- 1 siteCzechia
- Study dates
- 2 Feb 2025to 30 Apr 2025
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- 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.
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country.
About this study
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.
Read the detailed description
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.
- Conditions
- Artificial Intelligence
- Being tested
- Combat Medic Decision-Making with and without artificial intelligence assistance
- Keywords
- artificial intelligence, combat medicine, decision-making, mechanical ventilation
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Combat Medic Decision-Making with and without artificial intelligence assistanceOther
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Other: Combat Medic Decision-Making with and without artificial intelligence assistance |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 1 hour
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted28 Jan 2025
- Study start2 Feb 2025
- Primary completion31 Mar 2025
- Study completion30 Apr 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)Papers the sponsor has linked to this study.
- Other trials for Artificial IntelligenceStudies of the same condition, open and under way.
- More trials run by Charles University, Czech RepublicThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Other
- Allocation
- Not applicable
- Intervention model
- Single group
- Model description
- Crossover assignment (each participant acts as their own control in scenarios with and without artificial intelligence)
- Masking
- None (open label)
- Enrolment
- 42 participants (actual)
Sponsor and investigators
- Lead sponsor
- Charles University, Czech Republic
- Sponsor type
- University, hospital or other
- Collaborators
- Czech Technical University in Prague
- Responsible party
- Principal investigator, Michal Soták, Charles University, Czech Republic
- Investigators
- Michal Soták, M.D., Ph.D., Principal investigator, Charles University, Czech Republic
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06796036
- Sponsor's protocol ID
- FieldAI
- Acronym
- FieldAI
- Status last verified
- May 2025
- First submitted
- 22 Jan 2025
- Last updated
- 13 May 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Individual participant data will not be shared due to concerns regarding participant confidentiality, data privacy, and the sensitive nature of the study involving military personnel. Aggregate data and study findings will be shared through peer-reviewed publications and presentations.
Publications
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.
PubMed 37948287Preiksaitis 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.
PubMed 38728687
Study at a glance
From the registry record, last updated by the sponsor on 13 May 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Conversational AI in Tactical Casualty Care: Baseline GPT-4o Improves Combat Medic Decision-Making
- ClinicalTrials.gov ID
- NCT06796036
- Acronym / sponsor ID
- FieldAI / FieldAI
- Conditions
- Artificial Intelligence
- Keywords
- artificial intelligence, combat medicine, decision-making, mechanical ventilation
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 42 participants (actual)
- Lead sponsor
- Charles University, Czech RepublicUniversity, hospital or other
- Collaborators
- Czech Technical University in Prague
- Study start
- 2 Feb 2025 (actual)
- Primary completion
- 31 Mar 2025 (actual)
- Study completion
- 30 Apr 2025 (actual)
- First posted
- 28 Jan 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
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.
Study design
- Primary purpose
- Other
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
- Model description
- Crossover assignment (each participant acts as their own control in scenarios with and without artificial intelligence)
| Arm | Type | Receives |
|---|---|---|
| Combat Medic Decision-Making with and without AI Assistance | Experimental | Other: Combat Medic Decision-Making with and without artificial intelligence assistanceAll 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (5 listed)
- 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.
Key exclusion criteria (3 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Combat Medic Decision-Making with and without artificial intelligence assistanceParticipants 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. | Other | Combat Medic Decision-Making with and without AI Assistance |
Primary outcome
Accuracy of ventilator settings
Time frame: 1 hour
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.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 13 May 2025.
- Results publication: PubMed 37948287Nemeth 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.
- Results publication: PubMed 38728687Preiksaitis 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.
- Individual participant dataSharing planned: No. Individual participant data will not be shared due to concerns regarding participant confidentiality, data privacy, and the sensitive nature of the study involving military personnel. Aggregate data and study findings will be shared through peer-reviewed publications and presentations.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Michal Soták, M.D., Ph.D. principal investigator
Charles University, Czech Republic
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Combat Medic Decision-Making with and without artificial intelligence assistance in people with Artificial Intelligence, run by Charles University, Czech Republic at 1 site in Czechia.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 13 May 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Czechia. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of ventilator settings (time frame: 1 hour). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06796036, which also lists 2 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 13 May 2025.