DISASTER MEDICINE No. 2 •2026
doi: 10.33266/2070-1004-2026-2
Review article
Current Opportunities for Artificial Intelligence Technologies in First Aid
Litvin A.A. 1, Dezhurnyy L.I. 2,3, Radovnya M.V. 1, Khrushchova L.V. 1, Zakurdaeva A.Y. 3,4
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1 Gomel State Medical University, Gomel, Republic of Belarus
2 Institute of Advanced Medical Training of the Pirogov National Medical and Surgical Center of the Ministry of Health of the Russian Federation, Moscow, Russian Federation
3 Russian Society of First Aid, Moscow, Russian Federation
4 Sechenov First Moscow State Medical University, Moscow, Russian Federation
UDC 614.88:004.8:614.2
P. 85-92
Summary. The aim of the study is to analyze current research on the use of artificial intelligence technologies in first aid and to identify contradictions, limitations, and promising areas for future development.
Research methods – a systematic literature search in the PubMed, Google Scholar, SciSpace, and e-library databases for the period of 2015–2025. A total of 123 publications were selected, of which the 30 most relevant works were subjected to in-depth analysis.
Research results and their analysis. The article presents the state of the art in applying artificial intelligence technologies for first aid. The structure of AI-based diagnostic and decision-making systems is considered. Significant limitations, such as insufficient validation on independent samples, ethical and legal barriers, and model interpretability issues, are indicated. The challenges in current approaches to the development and implementation of AI tools for emergency situations are presented. A conclusion is made about the significant potential of AI to enhance the quality of first aid, while emphasizing the necessity of large-scale prospective studies, the development of safety standards, and a regulatory framework for the implementation of research results.
Key words: artificial intelligence, cardiopulmonary resuscitation, emergency medical care, first aid, machine learning, prehospital stage.
For citation: Litvin A.A., Dezhurny L.I., Radovnya M.V., Khrushchova L.V., Zakurdaeva A.Y. Current Opportunities for Artificial Intelligence Technologies in First Aid. Meditsina Katastrof = Disaster Medicine. 2026;2:85-92 (In Russ.). doi:10.33266/2070-1004-2026-2-85-92
СПИСОК ИСТОЧНИКОВ / REFERENCES
- Blomberg S.N., Folke F., Ersbøll A.K., et al. Machine Learning as a Supportive Tool to Recognize Cardiac Arrest in Emergency Calls. Resuscitation. 2019;138:322–329. Doi: 10.1016/j.resuscitation.2019.01.015.
- Kim D.H., Shin S.D., Ro Y.S., et al. A Novel Artificial Intelligence–Enhanced Digital Network for Prehospital Emergency Support: Community Intervention Study. Journal of Medical Internet Research. 2025;27:e58177. Doi: https://doi.org/10.2196/58177.
- Islam S., Rjoub G., Elmekki H., et al. Machine Learning Innovations in CPR: a Comprehensive Survey on Enhanced Resuscitation Techniques. Artificial Intelligence Review. 2025;58:art.233. Doi: https://doi.org/10.1007/s10462-025-11214-w.
- Coult J., Blackwood J., Rea T.D., et al. ECG-Based Prediction of Shock-Refractory Ventricular Fibrillation during Resuscitation without Interrupting CPR. Circulation: Arrhythmia and Electrophysiology. 2026;19;2:e014558. Doi: 10.1161/CIRCEP.125.014558.
- Liu Y., Zhang X., Wang L. Artificial Intelligence Technology-Based Medical Information Processing and Emergency First Aid Nursing Management. Computational and Mathematical Methods in Medicine. 2022;2022:Art.ID 8677118. Doi: 10.1155/2022/8677118.
- Toy J., Stettler M., Ghobrial M., et al. Use of Artificial Intelligence to Support Prehospital Traumatic Injury Care: a Scoping Review. Journal of The American College of Emergency Physicians. 2024 Sep 4;5;5:e13251. Doi: 10.1002/emp2.13251.
- Ventura L., Rossetti S., Giannini M.B., et al. Artificial Intelligence in Emergency Trauma Care: A Preliminary Scoping Review. Medical Devices: Evidence and Research. 2024;17:257–271. Doi: 10.2147/mder.s467146.
- Селиверстов П.А., Багненко С.Ф., Мирошниченко А.Г. и др. Возможности использования технологий искусственного интеллекта в догоспитальной травматологической помощи // Неотложная медицинская помощь. журнал им. Н.В. Склифосовского. 2025. Т.14. № 3. С. 609–618 [Seliverstov P.A., Bagnenko S.F., Miroshnichenko A.G., et al. Possibilities of using Artificial Intelligence Technologies in Pre-Hospital Trauma Care. Neotlozhnaya Meditsinskaya Pomoshch’. Zhurnal im. N.V. Sklifosovskogo = Russian Sklifosovsky Journal of Emergency Medical Care (In Russ.)]. Doi: 10.23934/2223-9022-2025-14-3-609-618.
- Abo-Zahhad M., Ahmed S.M., Elnahas O. Development of an AI-powered AR Glasses System for Real-Time First aid Guidance in Emergency Situations. Biodata Mining. 2025;18:Art.59. Doi: 10.1186/s13040-025-00473-6.
- Gulati A., Sharma R., Kumar P. A Context-Aware Emergency Assistance Chatbot Employing Recurrent Neural Networks for Personalized First Aid Guidance. Proceedings of the 2024 International Conference on Emerging Trends in Microelectronics, Power Systems and Signal Processing (ICEMPS). 2024. P. 1–6. Doi: 10.1109/icemps60684.2024.10559306.
- Bushuven S., Benteler L., Dethlefs M., et al. “ChatGPT, can you Help me Save my Child’s Life?” – Diagnostic Accuracy and Supportive Capabilities to Lay Rescuers by ChatGPT in Prehospital Basic Life Support and Paediatric Advanced Life Support Cases – an In-silico Analysis. Journal of Medical Systems. 2023 Nov 21;47;1:123. Doi: 10.1007/s10916-023-02019-x.
- Fromm J., Eyilmez K., Baßfeld M., et al. The Potential of Augmented Reality for Improving Occupational First Aid. Wirtschaftsinformatik und Angewandte Informatik. 14th International Conference on Wirtschaftsinformatik, Siegen, February 24-27 2019. Siegen, 2019.
- Chee M.L., Chowdhury A., Ong M.E.H., et al. Artificial Intelligence and Machine Learning in Prehospital Emergency Care: a Systematic Scoping Review. IScience. 2023 Jul 17;26;8:107407. Doi: 10.1101/2023.04.25.23289087.
- Ahammed T., Rahman M.S., Islam M.R. Smart Health Software to Support Rescue Personnel in Emergency Situations. Proceedings of the 2024 International Conference on Smart Systems and Technologies (SST). 2024. P. 1–5. Doi: 10.1109/sst61991.2024.10755467.
- Perry Z., Barak O., Levy A., et al. Artificial Intelligence-powered Mobile Tool for Burn Injury Evaluation for First Responders. Journal of Burn Care & Research. 2024;45;1:S240. Doi: 10.1093/jbcr/irae036.240.
- Shafaf N., Malek H. Applications of Machine Learning Approaches in Emergency Medicine; a Review Article. Archives of Academic Emergency Medicine. 2019;7;1:e34. Doi: 10.22037/aaem.v7i1.410.
- Mensah J., Agyemang K., Owusu-Ansah E., et al. All you Need is Context: Clinician Evaluations of various iterations of a Large Language Model-Based First Aid Decision Support Tool in Ghana. medRxiv. 2024 Sep 18;5:e65727. Doi: 10.1101/2024.04.03.24305276.
- Yazaki T., Nakamura Y., Ishikawa H., et al. Emergency Patient Triage Improvement through a Retrieval-Augmented Generation Enhanced Large-Scale Language Model. Prehospital Emergency Care. 2025;29;3:203-209. Doi: 10.1080/10903127.2024.2374400.
- Saban M., Shamir R.R., Bitan Y., et al. Machine Learning Models Powered by Emergency Medical Services Data Enhance Stroke Triage in Prehospital Settings. Scientific Reports. – 2026 Feb 3;16;1:7139. Doi: 10.1038/s41598-026-37069-x.
- Cho K.J., Kwon O., Kwon J.M., et al. Effect of Applying a Real-Time Medical Record Input Assistance System with Voice Artificial Intelligence on Triage Task Performance in the Emergency Department: Prospective Interventional Study. JMIR Medical Informatics. 2022;10;11:e39892. Doi: 10.2196/39892.
- Aqavil-Jahromi M., Seyedhosseini J., Bozorgi F., et al. Can ChatGPT, Bing, and Bard Save Lives? Evaluation of Correctness and Reliability of Chatbots in Teaching Bystanders to Help Victims. Research Square. June 2024. Doi: 10.21203/rs.3.rs-4518310/v1. URL: https://www.researchgate.net/publication/381630675_Can_ChatGPT_Bing_and_Bard_save_lives_Evaluation_of_correctness_and_reliability_of_chatbots_in_teaching_bystanders_to_help_victims.
- Anwar H., Al-Khateeb B., Hassan M. Towards Building a Chatbot-Based First Aid Service in Arabic Language. Journal of Advanced Research in Applied Sciences and Engineering Technology. 2024;45;2:1–10. Doi: 10.37934/araset.45.2.110.
- Franc J.M., Verde M., Gallardo A.R. Accuracy of a Commercial Large Language Model (ChatGPT) to Perform Disaster Triage of Simulated Patients Using the Simple Triage and Rapid Treatment (START) Protocol: Gage Repeatability and Reproducibility Study. Journal of Medical Internet Research. 2024; 26: e55648. Doi: 10.2196/55648.
- Franc J.M., Verde M., Gallardo A.R. Repeatability, Reproducibility, and Diagnostic Accuracy of a Commercial Large Language Model (ChatGPT) to Perform Disaster Triage Using the Simple Triage and Rapid Treatment (START) Protocol. Disaster Medicine and Public Health Preparedness. Cambridge, Cambridge University Press, 2024. Doi: 10.1017/dmp.2024.194.
The material was received 10.03.26; the article after peer review procedure 25.03.26; the Editorial Board accepted the article for publication 16.06.26
