Mallon Odhran, Lippert Freddy, Stassen Willem, Ong Marcus Eng Hock, Dolkart Caitlin, Krafft Thomas, Pilot Eva
Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands.
Falck, Copenhagen, Denmark.
Front Public Health. 2025 Jun 20;13:1604231. doi: 10.3389/fpubh.2025.1604231. eCollection 2025.
Improvements in prehospital emergency care have the potential to transform patient outcomes globally, but particularly within low-and middle-income countries. Whilst 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.
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.
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.
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. Whilst 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.
https://doi.org/10.17605/OSF.IO/9VS2M, osf.io/9vs2m.
院前急救护理的改善有可能改变全球患者的治疗结果，尤其是在低收入和中等收入国家。虽然人工智能正在许多医疗环境中得到应用，但对于其在院前急救护理系统中的使用情况却知之甚少。本综述旨在揭示人工智能目前在低收入和中等收入国家的院前急救医疗服务中是如何被使用的，并评估其对未来发展的影响。
对使用人工智能模型用于低收入和中等收入国家院前急救护理的同行评审文章进行了综述。检索了Medline、Global Health、Embase、CINAHL和Web of Science，以查找2014年1月至2024年7月期间发表的研究。数据被提取、整理并以表格形式和叙述性综述呈现。本综述按照PRISMA-ScR指南进行报告。
该研究纳入了16篇文章。大多数研究在中国进行，一半的研究使用了深度学习模型。评估调度预测的文章最为常见，不过人工智能工具也用于分类和疾病预测。在所有选定的研究中，样本量存在显著差异。总体而言，除两项研究外，机器学习算法在其他所有研究中的表现均优于其他比较方法。
局限性包括仅分析以英文发表的文章。此外，那些未将模型识别为人工智能工具，或在标题或摘要中未明确提及低收入和中等收入国家的研究可能被无意中排除。虽然人工智能可以显著改善院外环境中的患者护理，但这项技术的持续发展需要适当考虑这些国家的当地社会文化背景和挑战，同时使用完整的、针对特定人群的数据集。需要进一步的研究来支持该领域的进展，并促进全民健康覆盖的实现。
