人工智能在紧急情况和灾难分流中的应用:系统综述。

IF 3.5 2区 医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH BMC Public Health Pub Date : 2024-11-18 DOI:10.1186/s12889-024-20447-3
Azadeh Tahernejad, Ali Sahebi, Ali Salehi Sahl Abadi, Mehdi Safari
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引用次数: 0

摘要

导言和目标:由于全球灾害和突发事件呈增长趋势,受伤人数增加,传统的分诊方法面临挑战,现代智能分诊系统应运而生。本研究的主要目的是调查人工智能和技术在灾害和紧急情况下伤员分流中的应用,以及实施智能分流系统所面临的挑战:本研究是一项系统性综述,遵循 PRISMA 指南。本研究的方案已在 PROSPERO 注册,代码为 CRD42023471415。为查找相关研究,我们在 PubMed、Scopus 和 Web of Science (ISI) 等数据库中进行了无时间限制的搜索,直至 2024 年 9 月。科学搜索引擎谷歌学术(Google Scholar)和最终文章的参考文献均由人工阅读,以进行最终审查:搜索发现了 2,630 篇文章,最终筛选出 19 项关于人工智能分诊的高质量研究,这些研究通过优化资源管理和实时数据传输改善了患者护理。OpenPose和YOLO等人工智能算法提高了大规模伤亡事件中的效率,而电子分诊系统则实现了持续的生命体征监测和更快的分诊。人工智能工具在诊断 COVID-19 时表现出极高的准确率(94.57%)。实施智能分诊系统面临着信任问题、培训需求、设备短缺和数据隐私问题等挑战:结论:利用人工智能开发评估系统可为灾难中的伤员提供及时治疗和更好的复苏服务。对于未来的研究,我们建议设计智能分诊系统,以消除灾害中儿童和残疾人分诊的障碍。
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Application of artificial intelligence in triage in emergencies and disasters: a systematic review.

Introduction and objective: Modern and intelligent triage systems are used today due to the growing trend of disasters and emergencies worldwide and the increase in the number of injured people facing the challenge of using traditional triage methods. The main objective of this study is to investigate the application of artificial intelligence and Technology in the triage of patients injured by disasters and emergencies and the challenges of the implementation of intelligent triage systems.

Method: The present study is a systematic review and follows PRISMA guidelines. The protocol of this study was registered in PROSPERO with the code CRD42023471415. To find relevant studies, the databases PubMed, Scopus and Web of Science (ISI) were searched without a time limit until September 2024. The scientific search engine Google Scholar and the references of the final articles were read manually for the final review.

Results: The search identified 2,630 articles, narrowing down to 19 high-quality studies on AI in triage, which improved patient care through optimized resource management and real-time data transmission. AI algorithms like OpenPose and YOLO enhanced efficiency in mass casualty incidents, while e-triage systems allowed for continuous vital sign monitoring and faster triaging. AI tools demonstrated high accuracy in diagnosing COVID-19 (94.57%). Implementing intelligent triage systems faced challenges such as trust issues, training needs, equipment shortages, and data privacy concerns.

Conclusion: Developing assessment systems using artificial intelligence enables timely treatment and better resuscitation services for people injured in disasters. For future studies, we recommend designing intelligent triage systems to remove the obstacles in triaging children and disabled people in disasters.

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来源期刊
BMC Public Health
BMC Public Health 医学-公共卫生、环境卫生与职业卫生
CiteScore
6.50
自引率
4.40%
发文量
2108
审稿时长
1 months
期刊介绍: BMC Public Health is an open access, peer-reviewed journal that considers articles on the epidemiology of disease and the understanding of all aspects of public health. The journal has a special focus on the social determinants of health, the environmental, behavioral, and occupational correlates of health and disease, and the impact of health policies, practices and interventions on the community.
期刊最新文献
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