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Evaluating the Impact of Artificial Intelligence on Medical Diagnosis and Physiotherapy Treatment: A Systematic Review

Authors :
Shahul Hameed Pakkir Mohamed
Source :
International Journal of Physiotherapy, Vol 11, Iss 4 (2024)
Publication Year :
2024
Publisher :
Vasinformatics, 2024.

Abstract

Background: Artificial Intelligence (AI) can assist healthcare professionals with various aspects of patient care and healthcare systems. This systematic review aims to assess the role of AI in healthcare today, emphasizing how it may impact medical diagnosis and physiotherapy treatment. Methods: The electronic databases searched included PubMed, Scopus, Embase, and Web of Science. A thorough search of relevant literature published in peer-reviewed journals is part of the review, focusing on research examining AI's application in clinical trials, medical imaging, drug development, physiotherapy, and disease management. The search initially identified 255 studies. After screening, 30 studies met the inclusion criteria. Results: This review provides evidence that AI systems can help with these aspects of medical diagnosis and therapy, especially in medical imaging, where they can precisely detect anomalies and lesions in images. However, the review also highlights the possible drawbacks and restrictions of using AI in healthcare. These include the risk that AI algorithms may exacerbate pre-existing health disparities and the necessity of ensuring that AI is implemented fairly, safely, and effectively for every patient. Conclusion: AI systems can assist with disease management, medication development, medical imaging, clinical trials, medical diagnosis, and physiotherapy. The assessment finds that caution must be exercised in their application to ensure that potential advantages are maximized while possible dangers and drawbacks are minimized.

Details

Language :
English
ISSN :
23495987 and 23488336
Volume :
11
Issue :
4
Database :
Directory of Open Access Journals
Journal :
International Journal of Physiotherapy
Publication Type :
Academic Journal
Accession number :
edsdoj.139139bb6cca445f846b4d77d2819120
Document Type :
article
Full Text :
https://doi.org/10.15621/ijphy/2024/v11i4/1521