A survey of machine learning techniques in medical applications

• URL: http://arxiv.org/abs/2302.13268v5
• Date: Tue, 30 Jul 2024 10:29:24 GMT
• Title: A survey of machine learning techniques in medical applications
• Authors: M. Keramy, K. Jahanian, R. Sani, A. Agha, I. Dehzangy, M. Yan, H. Rokni,
• Abstract summary: The exponential growth of medical data has surpassed the capacity for manual analysis, prompting increased interest in automated data analysis and processing. ML algorithms, capable of learning from data with minimal human intervention, are particularly well-suited for medical data analysis and interpretation. One significant advantage of ML is the reduced cost of collecting labeled training data necessary for supervised learning.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: In recent years, machine learning (ML) has emerged as a powerful tool for solving a wide range of problems, including medical decision-making. The exponential growth of medical data over the past two decades has surpassed the capacity for manual analysis, prompting increased interest in automated data analysis and processing. ML algorithms, capable of learning from data with minimal human intervention, are particularly well-suited for medical data analysis and interpretation. One significant advantage of ML is the reduced cost of collecting labeled training data necessary for supervised learning. While numerous studies have explored the applications of ML in medicine, this survey specifically focuses on the use of ML across various medical research fields. We provide a comprehensive technical overview of existing studies on ML applications in medicine, highlighting the strengths and limitations of these approaches. Additionally, we discuss potential research directions for future exploration. These include the development of more sophisticated reward functions, as the accuracy of the reward function is crucial for ML performance, the integration of ML with other techniques, and the application of ML to new and emerging areas in genomics research. Finally, we summarize our findings and present the current state of the field and the future outlook for ML in medical application.

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