RETRACTED ARTICLE: A systematic review and applications of how AI evolved in healthcare

Machine Learning (ML) is a specialized domain within the broader science of Artificial Intelligence. Machine Learning facilitates the acquisition of knowledge by machines through the analysis of data. As a result, ML empowering them to generate predictions or make informed decisions. Machine learnin...

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Bibliographic Details
Published in:Optical and quantum electronics 2024, Vol.56 (3), Article 301
Main Authors: Divya, K., Kannadasan, R.
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Characterization and Evaluation of Materials
Computer Communication Networks
Electrical Engineering
Health care
Lasers
Machine learning
Optical Devices
Optics
Performance measurement
Photonics
Physics
Physics and Astronomy
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Summary:Machine Learning (ML) is a specialized domain within the broader science of Artificial Intelligence. Machine Learning facilitates the acquisition of knowledge by machines through the analysis of data. As a result, ML empowering them to generate predictions or make informed decisions. Machine learning has found extensive application across diverse healthcare domains, encompassing the areas of disease diagnosis, prognosis, treatment, and disease management. Nevertheless, the integration and implementation of machine learning models in healthcare face numerous challenges and limitations. These include issues related to data governance, data quality, legal considerations, ethical concerns, and interpretability. This article presents a thorough examination of diverse machine learning concepts and their potential implications in the healthcare domain. Furthermore, the paper explores various categories of machine learning models and examines the diverse range of algorithms that can be employed to address specific tasks. In addition, this paper presents a precis of various machine learning techniques and their respective applications in the field of healthcare, utilizing diverse datasets and performance metrics. Ultimately, this study examines the barriers and potential remedies surrounding the implementation and utilization of machine learning in the healthcare sector. Additionally, offers suggestions for future research avenues.
ISSN:0306-8919
1572-817X
DOI:10.1007/s11082-023-05798-2