Adoption of machine learning for medical diagnosis

The healthcare industry has historically been an early adopter of technology advancements and has reaped significant benefits. Machine learning (an artificial intelligence subset) is being used in a variety of health-related fields, including the invention of new medical treatments, the management of patient data and records, and the treatment of chronic diseases. One of the most important uses of machine learning in healthcare is the detection and diagnosis of diseases and conditions that are otherwise difficult to identify. This can range from tumors that are difficult to detect in their early stages to other hereditary illnesses. This research identifies and discusses the various usages of machine learning in medical diagnosis.

Content

Author and article information

Journal

Title: ScienceOpen Preprints

Publisher: ScienceOpen

Publication date (Electronic preprint): 25 September 2021

Affiliations

[1 ] Saudia Arabia

Author notes

[* ]Email: yiroyo1235@ 123456tmednews.com .

Author information

Mohammed Yousef Shaheen https://orcid.org/0000-0002-2993-2632

Article

DOI: 10.14293/S2199-1006.1.SOR-.PPHMKA6.v1

SO-VID: 7e5d7c22-bd84-4b12-855e-869c466f11a8

License:

This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

History

Date received : 25 September 2021

Data availability: Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

ScienceOpen disciplines: Artificial intelligence,Health & Social care

Keywords: CNN,Machine learning,Artificial Intelligence,Diagnosis,Healthcare,AI,ANN

Comments

Sandeep Trivedi wrote:

The manuscript tries to identify and explore the numerous applications of machine learning in medical diagnostics. This is a well-researched and very written research with an essential contribution. After the problems listed below have been rectified, I would endorse publishing.

a) Author can replace contractions such as “it’s” and “can’t” with “it is” and “can not”.

b) A table or figure detailing the various applications could improve the paper's readability.

c) There are some limitations that slow down ML usage. Authors can include a separate section discussing the limitations of adopting ML.

d) The title can be made more consistent by replacing the word “adoption” with the word “applications”.

2022-09-08 23:16 UTC