Usmani, U.A. and Jaafar, J. (2022) Machine Learning in Healthcare: Current Trends and the Future. Lecture Notes in Electrical Engineering, 758. pp. 659-675. ISSN 18761100
Full text not available from this repository.Abstract
Today, an abundance of electronically stored medical image data and DL algorithms can be used to recognize and detect patterns and anomalies in this kind of dataset. Computers and algorithms can interpret the imaging data as a very qualified radiologist can see irregular skin, lesions, tumours and brain bleeds. Consequently, the use of AI/ML tools/platforms to help radiologists is poised to grow exponentially. This approach addresses a vital issue in the healthcare sector as well-trained radiologists are challenging to come by worldwide. These professional professionals are, in most cases, under tremendous pressure due to the influx of digital medical data. We analyze and address the current state of A.I. applications in healthcare. A.I. can be applied to various healthcare data forms (structured and unstructured). Popular A.I. techniques include machine learning for structured data such as classic support vectors and neural networks, modern in-depth learning unstructured data natural language processing. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Item Type: | Article |
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Additional Information: | cited By 6; Conference of 1st International Conference on Artificial Intelligence for Smart Community, AISC 2020 ; Conference Date: 17 December 2020 Through 18 December 2020; Conference Code:286319 |
Uncontrolled Keywords: | Data handling; Learning algorithms; Learning systems; Medical computing; Medical imaging; Natural language processing systems; Support vector machines, 'current; Disease detection; Healthcare trend; Imaging data; Learning tool; Machine learning tool; Machine-learning; Medical images datum; ML disease detection; Robotics surgery, Robotic surgery |
Depositing User: | Mr Ahmad Suhairi UTP |
Date Deposited: | 19 Dec 2023 03:23 |
Last Modified: | 19 Dec 2023 03:23 |
URI: | https://khub.utp.edu.my/scholars/id/eprint/17402 |