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Ferdous, M.; Debnath, J.; Chakraborty, N., Machine Learning Algorithms in Healthcare: A Literature Survey, Proceedings of the 11th Int. Conf. on Computing, Communication and Networking Technologies (ICCCNT), 2020, 1–6.

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Article

Artificial Intelligence Algorithms for Healthcare Services

1Faculty of Computer Science, The American University in Cairo, Egypt

2Faculty of Engineering, University of Peloponnese, Patras, Greece

3Faculty of Computer& Info. Sciences, Ain Shams University, Egypt


American Journal of Applied Mathematics and Statistics. 2023, Vol. 11 No. 2, 70-76
DOI: 10.12691/ajams-11-2-5
Copyright © 2023 Science and Education Publishing

Cite this paper:
Dalia K. A. A. Rizk, Hoda M. Hosny, Michael Gr. Voskoglou, El-Sayed M. El-Horbaty, Abdel-Badeeh M. Salem. Artificial Intelligence Algorithms for Healthcare Services. American Journal of Applied Mathematics and Statistics. 2023; 11(2):70-76. doi: 10.12691/ajams-11-2-5.

Correspondence to: Michael  Gr. Voskoglou, Faculty of Engineering, University of Peloponnese, Patras, Greece. Email: voskoglou@teiwest.gr

Abstract

A range of healthcare and medical sectors can benefit from the intelligent concepts, approaches, techniques, and algorithms provided by artificial intelligence (AI) paradigms. AI could streamline patient flow or treatment strategies and give doctors virtually all the data they require to make wise medical and healthcare decisions. Healthcare is just beginning to undergo a significant change because of AI, starting with the creation of treatment strategies and moving through the augmentation of repetitive tasks through medication management or drug research. It can be used in a wide range of contexts, including data management, drug research, diabetic treatment, and digital consultation. Furthermore, the benefits of AI enable the investigation of enormous datasets by algorithms in situations like those involving inaccessible geographic regions. Most other emerging technologies fall under the general heading of AI. Due to their significance in identifying patients with chronic diseases, their capacity to identify risk scenarios, and their ability to foster the development of novel remedies, these new technologies must be integrated into healthcare. As a result, a set of rules that are too complex and extensive for a human programmer to handle is given into an AI software to detect the similarities. The main objective of this paper is to analyze the major known AI algorithms and to show their usage with healthcare services.

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