نوع مقاله : مروری
نویسندگان
1 کارشناسی ارشد مدیریت خدمات بهداشتی ودرمانی،گروه مدیریت سلامت، سیاست و اقتصاد، دانشکده مدیریت و اطلاع رسانی پزشکی، دانشگاه علوم
2 استاد مهندسی صنایع، دانشکده مهندسی صنایع، دانشگاه علم و صنعت ایران، تهران، ایران.
3 دکتری مهندسی صنایع، گرایش مدیریت سیستم و بهرهوری، دانشکده مهندسی صنایع، دانشگاه علم و صنعت ایران، تهران، ایران.
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Abstract
With the growing complexity of social conditions and the emergence of individual, family, and community crises, the need for rapid and effective interventions has become increasingly urgent. Social emergency programs, as vital structures, play an essential role in protecting individuals at risk. Artificial intelligence (AI), as a modern tool, offers significant potential for crisis prediction, data analysis, and rapid decision-making. This study aimed to systematically review the effects of AI in programs similar to social emergency services and to identify related opportunities and challenges.
Methods: A systematic search was conducted in PubMed, Scopus, Web of Science, Google Scholar, and national databases (SID, Magiran) for studies published between 2010 and June 2024. Peer-reviewed Persian and English articles on AI in social services and crisis interventions were included. Study quality was assessed using the CASP checklist, and thematic analysis was performed. Out of 1,151 retrieved articles, 8 met the inclusion criteria.
Findings: AI, particularly through machine learning, was shown to predict crises such as suicide risk, domestic violence, and adolescent runaways, enabling faster and more targeted interventions. It also enhanced the speed and accuracy of social workers’ decisions and reduced clients’ fear of social stigma. However, infrastructural weaknesses, privacy concerns, staff resistance, and risks of digital inequality were identified as major challenges.
Conclusion: While AI can improve the effectiveness of social emergency services, responsible integration requires ethical frameworks, strong oversight, staff training, and infrastructure development. Further local studies are recommended to ensure fair and sustainable adoption.
کلیدواژهها [English]
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