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Volume 12, Issue 3 (2024)
Health Educ Health Promot 2024, 12(3): 513-520 |
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Received: 2024/07/9 | Accepted: 2024/10/20 | Published: 2024/10/30
Received: 2024/07/9 | Accepted: 2024/10/20 | Published: 2024/10/30
How to cite this article
Mousavi Baigi S, Dahmardeh Kemmak F, Sarbaz M, Norouzi Aval R, Kimiafar K. Application of Artificial Intelligence in Occupational Therapy. Health Educ Health Promot 2024; 12 (3) :513-520
URL: http://hehp.daneshafarand.org/article-4-76014-en.html
URL: http://hehp.daneshafarand.org/article-4-76014-en.html
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Authors
S.F. Mousavi Baigi1 
, F. Dahmardeh Kemmak1 , M. Sarbaz2 , R. Norouzi Aval1 , Kh. Kimiafar *2
, F. Dahmardeh Kemmak1 , M. Sarbaz2 , R. Norouzi Aval1 , Kh. Kimiafar *2
1- “Student Research Committee†and “Department of Health Information Technology, School of Paramedical and Rehabilitation Sciencesâ€, Mashhad University of Medical Sciences, Mashhad, Iran
2- Department of Health Information Technology, School of Paramedical and Rehabilitation Sciences, Mashhad University of Medical Sciences, Mashhad, Iran
2- Department of Health Information Technology, School of Paramedical and Rehabilitation Sciences, Mashhad University of Medical Sciences, Mashhad, Iran
Abstract (3619 Views)
Aims: New developments in artificial intelligence offer promising prospects for transforming therapeutic approaches and enhancing outcomes for individuals with a range of abilities. Therefore, the aim of this systematic review was to investigate the applications of artificial intelligence in occupational therapy.
Information & Methods: In this systematic review, adhering to the PRISMA guidelines, we searched English-language studies regarding the use of artificial intelligence in occupational therapy, on February 18, 2024, using the databases PubMed, Embase, Scopus, and Web of Science.
Findings: Six eligible studies were included in this review. The artificial intelligence approaches used in these studies included artificial neural networks, multi-core learning models, deep learning models, machine learning models, and classification and regression trees. All the studies reported promising results regarding the use of artificial intelligence in evaluating and predicting return to work, alleviating symptoms, recovering social function, reducing disease recurrence, improving re-employment rates, and enhancing the overall health level of patients.
Conclusion: One of the most common issues with artificial intelligence models is their low accuracy and the potential for errors.
Information & Methods: In this systematic review, adhering to the PRISMA guidelines, we searched English-language studies regarding the use of artificial intelligence in occupational therapy, on February 18, 2024, using the databases PubMed, Embase, Scopus, and Web of Science.
Findings: Six eligible studies were included in this review. The artificial intelligence approaches used in these studies included artificial neural networks, multi-core learning models, deep learning models, machine learning models, and classification and regression trees. All the studies reported promising results regarding the use of artificial intelligence in evaluating and predicting return to work, alleviating symptoms, recovering social function, reducing disease recurrence, improving re-employment rates, and enhancing the overall health level of patients.
Conclusion: One of the most common issues with artificial intelligence models is their low accuracy and the potential for errors.
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