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Optical and Quantum Electronics
Erschienen in: Optical and Quantum Electronics 3/2024

01.03.2024

A systematic review and applications of how AI evolved in healthcare

verfasst von: K. Divya, R. Kannadasan

Erschienen in: Optical and Quantum Electronics | Ausgabe 3/2024

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Abstract

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.
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Metadaten
Titel
A systematic review and applications of how AI evolved in healthcare
verfasst von
K. Divya
R. Kannadasan
Publikationsdatum
01.03.2024
Verlag
Springer US
Erschienen in
Optical and Quantum Electronics / Ausgabe 3/2024
Print ISSN: 0306-8919
Elektronische ISSN: 1572-817X
DOI
https://doi.org/10.1007/s11082-023-05798-2

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