Artificial intelligence in medical emergencies: what clinical trials are starting to show
Keywords:
artificial intelligence, emergency department, triage, decision support, prehospital care, machine learning, response time, diagnostic accuracyAbstract
Artificial intelligence (AI) is rapidly entering emergency medicine, promising faster triage, more accurate diagnosis, and better allocation of scarce resources. Yet its real benefit depends on clinical trials rather than technical performance alone. This article synthesizes prospective and randomized studies summarized in Cochrane‑indexed and Cochrane‑searching systematic reviews, alongside individual emergency department (ED) and prehospital trials, to examine how AI performs in real emergency settings. We focus on AI‑driven triage, clinical decision support, and prehospital risk prediction, highlighting effects on response times, diagnostic accuracy, and patient outcomes. Across trials, AI tools frequently outperform traditional triage scores and standard decision support on accuracy‑related metrics, but gains in hard clinical outcomes are modest and context‑dependent. Methodological heterogeneity, limited randomization, and workflow integration issues temper current enthusiasm. Overall, AI appears most promising as an adjunct that accelerates and standardizes decision‑making rather than as an autonomous system, underscoring the need for larger, pragmatic trials.
References
1. Ahmadbekov , B., & Tuychibekov, S. (2026). Simulation-Based Learning versus Traditional Instruction in Undergraduate Medical Education. Journal of Clinical and Biomedical Research, 1(2), 19–28. Retrieved from https://www.medjournal.it.com/index.php/jcbr/article/view/95
2. Ahmadbekov, B., & Tuychibekov, S. (2026). Nanoparticle-Enabled Cancer Theranostics: Recent Advances in Diagnosis, Targeted Therapy, and Immunomodulation. Journal of Clinical and Biomedical Research, 1(2), 29–41. Retrieved from https://www.medjournal.it.com/index.php/jcbr/article/view/96
3. Ahmadbekov, B., & Tuychibekov, S. (2026). Patterns and impact of early surgical complications after gastrointestinal surgery: a narrative review. Journal of Clinical and Biomedical Research, 1(2), 51–59. Retrieved from https://www.medjournal.it.com/index.php/jcbr/article/view/98
4. Ahmadbekov, B., & Tuychibekov, S. (2026). Perioperative Outcomes and Prognostic Impact of Postoperative Complications in a 68 Patient Cohort Undergoing Colorectal Cancer Resection. Journal of Clinical and Biomedical Research, 1(2), 42–50. Retrieved from https://www.medjournal.it.com/index.php/jcbr/article/view/97
5. Egamberdiyeva, G. (2026). Seasonal Burden of Pediatric Otorhinolaryngologic Diseases: Comparative Patterns of Viral Infections, Acute Otitis Media, and Allergic Rhinitis. Journal of Clinical and Biomedical Research, 2(1), 240-246.
6. Egamberdiyeva, G., & Xoshimov, I. (2026). Preventing More Than Ear, Nose, and Throat: A Systematic Review of Complication Focused Preventive Strategies in Otorhinolaryngology and Their Somatic Systemic Parallels. Journal of Clinical and Biomedical Research, 2(1), 231-239.
7. Gochadze, A. L., & Irgasheva, M. D. (2016). Using clinical interactive games on lessons in medical colleges. Актуальные проблемы гуманитарных и естественных наук, (5-6), 26-28.
8. Irgasheva Maxbubaxon Davlatjon qizi. (2026). EFFECTIVENESS OF SIMULATION-BASED LEARNING IN NURSING EDUCATION: THEORETICAL AND PRACTICAL ASPECTS. Ethiopian International Journal of Multidisciplinary Research, 13(03), 22–26. Retrieved from https://www.eijmr.org/index.php/eijmr/article/view/5451
9. Qodirjonov, I. (2026). Learning curve and outcomes in pediatric robotic-assisted surgery: a seven-year single-center experience. International Journal of Clinical & Translational Medicine, 1(2), 24-30.
10. Qodirjonov, I. (2026). Postoperative Cerebrospinal Fluid Leak and Meningitis after Endoscopic Endonasal Skull Base Surgery: Complications at the Otorhinolaryngology–Neurosurgery Interface. International Journal of Clinical & Translational Medicine, 1(2), 16-23.
11. Tuychibekov, S. (2026). Postoperative Complications After Neck Surgery: Comparative Patterns and Statistical Approaches for Clinical Research. Journal of Clinical and Biomedical Research, 1(2), 1-9.
12. Valiyev, A. (2026). Medical complications following procedures in otorhinolaryngology: patterns, mechanisms, and management. International Journal of Clinical & Translational Medicine, 1(2), 53-63.
13. Valiyev, A. (2026). Medicine, Medical Education, and Hygiene: Interwoven Pillars of Modern Public Health. International Journal of Clinical & Translational Medicine, 1(2), 64-74.
14. Valiyev, A. (2026). Surgical Operations Powered by Robotic Technology: Present Realities and Future Horizons. International Journal of Clinical & Translational Medicine, 1(2), 42-52.
15. Иргашева M. (2025). Симуляция в клиническом сестринском образовании. Общество и инновации, 6(2/S), 107–112. https://doi.org/10.47689/2181-1415-vol6-iss2/S-pp107-112
16. Иргашева, М. Д. (2024). ОСОБЕННОСТИ ПЕРСОНАЛИЗИРОВАННОГО ОБУЧЕНИЯ. PEDAGOG, 7(11), 250-254.
17. Уразалиева, И. Р., & Иргашева, М. Д. (2021). ОПРЕДЕЛЕНИЕ СТЕПЕНИ ИНФОРМИРОВАННОСТИ ПАЦИЕНТОВ С САХАРНЫМ ДИАБЕТОМ О ПРОГРАММЕ УПРАВЛЕНИЯ ЗАБОЛЕВАНИЯМИ. Интернаука, (2-1), 50-51.
