<?xml version="1.0" encoding="UTF-8"?>
<doi_batch version="5.3.1" xmlns="http://www.crossref.org/schema/5.3.1" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:jats="http://www.ncbi.nlm.nih.gov/JATS1" xmlns:ai="http://www.crossref.org/AccessIndicators.xsd" xsi:schemaLocation="http://www.crossref.org/schema/5.3.1 http://www.crossref.org/schema/deposit/crossref5.3.1.xsd">
 <head>
  <doi_batch_id>aspg-28-2674-1792155167</doi_batch_id>
  <timestamp>20261016125247</timestamp>
  <depositor>
   <depositor_name>American Scientific Publishing Group</depositor_name>
   <email_address>admin@americaspg.com</email_address>
  </depositor>
  <registrant>American Scientific Publishing Group</registrant>
 </head>
 <body>
  <journal>
   <journal_metadata language="en">
    <full_title>Journal of Artificial Intelligence and Metaheuristics</full_title>
    <abbrev_title>JAIM</abbrev_title>
    <issn media_type="electronic">2833-5597</issn>
   </journal_metadata>
   <journal_issue>
    <publication_date media_type="online">
     <year>2024</year>
    </publication_date>
    <journal_volume>
     <volume>7</volume>
    </journal_volume>
    <issue>2</issue>
   </journal_issue>
   <journal_article publication_type="full_text">
    <titles>
     <title>Advancements and Future Directions in Machine Learning for Medical Diagnostics: A Comprehensive Review</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Basant</given_name>
      <surname>Sameh</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology (DHIET), Mansoura 35111, Egypt</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Nima</given_name>
      <surname>Khodadadi</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Civil and Architectural Engineering, University of Miami, Coral Gables, FL, USA</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Ehsan</given_name>
      <surname>khodadadi</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Chemistry and Biochemistry, University of Arkansas, Fayetteville, AR 72701, USA</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Marwa M.</given_name>
      <surname>Eid</surname>
      <affiliations>
       <institution>
        <institution_name>Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 35111, Egypt</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>S. K.</given_name>
      <surname>Towfek</surname>
      <affiliations>
       <institution>
        <institution_name>Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>Machine learning (ML) based techniques have enjoyed significant popularity in addressing the hostility of numerous problems in a range of applications, such as finance, marketing, production, environment, health care, and security. One of the most important distinctions between machine learning and human ways of thinking is their ability to observe patterns, make interpretations, reveal some hidden relationships, and analyze huge amounts of data. Machine learning (ML) technology can lead to improved specificity, sensitivity, predictability, and steadiness of such systems. Through this review, though, we will have an in-depth discourse on the application of machine learning in the field of medicine and how the latest technologies are mostly deployed in diagnostics. Medical applications that are widely used, including but not limited to machine learning solutions for medical chemistry, wearable sensors, cancer, the brain, and medical imaging, will be discussed in detail, with a focus on model adjustments to address the problems faced by the applications. In the course of the work, academics, practitioners, and decision-makers will have plenty of opportunities to utilize the findings, references, and insights of this study to improve their work and steer future research.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2024</year>
    </publication_date>
    <pages>
     <first_page>18</first_page>
     <last_page>31</last_page>
    </pages>
    <publisher_item>
     <item_number item_number_type="article-number">2674</item_number>
    </publisher_item>
    <ai:program name="AccessIndicators">
     <ai:license_ref applies_to="vor">https://creativecommons.org/licenses/by/4.0/</ai:license_ref>
    </ai:program>
    <doi_data>
     <doi>10.54216/JAIM.070202</doi>
     <resource>https://www.americaspg.com/journal/28/article/2674</resource>
     <collection property="text-mining">
      <item>
       <resource mime_type="application/pdf">https://www.americaspg.com/storage/91713458429.pdf</resource>
      </item>
     </collection>
    </doi_data>
   </journal_article>
  </journal>
 </body>
</doi_batch>
