SYSTEMATIC REVIEW article
Front. Public Health
Sec. Digital Public Health
Volume 13 - 2025 | doi: 10.3389/fpubh.2025.1604231
This article is part of the Research TopicAdvancing Healthcare AI: Evaluating Accuracy and Future DirectionsView all articles
Utilising Artificial Intelligence in Prehospital Emergency Care Systems in Low-and Middle-Income Countries: A Scoping Review
Provisionally accepted- 1Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands
- 2Falck, Copenhagen, Denmark
- 3Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, North East England, United Kingdom
- 4Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Capital Region of Denmark, Denmark
- 5Division of Emergency Medicine, University of Cape Town, Cape Town, South Africa
- 6Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore
- 7Flare Emergency Services, Nairobi, Kenya
- 8Rescue.co, Nairobi, Kenya
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Introduction: Improvements in prehospital emergency care have the potential to transform patient outcomes globally, but particularly within low-and middle-income countries. While artificial intelligence is being implemented in many healthcare settings, little is known about its use in prehospital emergency care systems. This scoping review aims to uncover how artificial intelligence is currently being used within the prehospital emergency medical services of low-and middle-income countries and assess the implications for future development. Methods: A review of peer-reviewed articles using any artificial intelligence models in prehospital emergency care in low-and middle-income countries was carried out. Medline, Global Health, Embase, CINAHL and Web of Science were searched for studies published between January 2014 and July 2024. Data were extracted, collated and presented in table format and as a narrative synthesis. This scoping review is reported using the PRISMA-ScR guidelines. Results: Sixteen articles were included in the study. Most studies were conducted in China and deep learning models were used in half of the studies. Articles assessing dispatch forecasting were the most common, although artificial intelligence tools are also utilised in classification and disease prediction. There was significant variation in sample sizes throughout the selected studies. Overall, machine learning algorithms outperformed other comparator methods when they were used in all but two studies. Discussion: Limitations included only analysing articles published in English. Additionally, studies that did not identify the model as an artificial intelligence tool, or did not explicitly mention a LMIC in the title or abstract may have been inadvertently excluded. While artificial intelligence can significantly benefit patient care in out-of-hospital settings, the continued development of this technology requires proper consideration for the local sociocultural contexts and challenges in these countries, along with using complete, population-specific datasets. Further research is needed to support advancements in this field and promote the realisation of universal health coverage.
Keywords: artificial intelligence, machine learning, prehospital, Emergency Medical Services, LMIC, emergency patient care
Received: 01 Apr 2025; Accepted: 02 Jun 2025.
Copyright: © 2025 Mallon, Lippert, Stassen, Ong, Dolkart, Krafft and Pilot. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Odhran Mallon, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, 6211 LK, Netherlands
Eva Pilot, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, 6211 LK, Netherlands
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.