We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Advancements in medical diagnosis and treatment through machine learning: A review.
- Authors
Ahsan, Mohammad; Khan, Anam; Khan, Kaif Rehman; Sinha, Bam Bahadur; Sharma, Anamika
- Abstract
The aptness of machine learning (ML) to learn from large datasets, discover trends, and make predictions has demonstrated its potential to metamorphose the medical field. Medical data analysis with ML algorithms can improve patient outcomes in terms of both treatment and diagnosis. This paper investigates the numerous possibilities of ML in the medical industries, including radiology, pathology, genomics, and clinical decision‐making. It also goes over the benefits and drawbacks of ML in various sectors as well as the limitations that come with its application. It illustrates the potential advantages of ML, such as better accuracy and efficiency in diagnosis and individualized treatment programs, through a review of previous studies. Lastly, it provides perspectives on prospective advancements and prospects for the discipline. This study also intends to investigate the applications of deep learning (DL) in the medical field. DL algorithms have performed exceptionally satisfactorily in several healthcare‐related fields. The main conclusions of the study are summarized, and their ramifications for the healthcare sector are discussed in this paper's conclusion. This paper intends to contribute to a greater understanding of the prevailing state of the discipline and the possibility for future developments by emphasizing the prospects of these methodologies to alter medical study and clinical practice.
- Subjects
DIAGNOSIS; THERAPEUTICS; DEEP learning; CONVOLUTIONAL neural networks; MACHINE learning; TREATMENT programs
- Publication
Expert Systems, 2024, Vol 41, Issue 3, p1
- ISSN
0266-4720
- Publication type
Article
- DOI
10.1111/exsy.13499