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  <doi_batch_id>aspg-41-3407-1792223353</doi_batch_id>
  <timestamp>20261017074913</timestamp>
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   <journal_metadata language="en">
    <full_title>Metaheuristic Optimization Review</full_title>
    <abbrev_title>MOR</abbrev_title>
    <issn media_type="electronic">3066-280X</issn>
   </journal_metadata>
   <journal_issue>
    <publication_date media_type="online">
     <year>2024</year>
    </publication_date>
    <journal_volume>
     <volume>2</volume>
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    <issue>1</issue>
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   <journal_article publication_type="full_text">
    <titles>
     <title>Artificial Intelligence for Skin Cancer Detection: A Review of Current Approaches</title>
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    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Abdelaziz A.</given_name>
      <surname>Abdelhamid</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, 11566, Cairo, Egypt</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>AI is emerging as a potential tool for revolutionizing dermatology in the early detection and diagnosis of skin cancers. This Review looks into the most recent innovations in AI technology, such as machine learning, deep learning, and explainable AI (XAI)) Moreover, it presents how one can achieve diagnostic accuracy similar to or exceeding that of well-experienced dermatologists. Access to such diagnostic tools in under resourced areas has been enhanced, inter-observer variability has increased, and workflows in clinical practice have been streamlined. Nevertheless, issues regarding diversity in data, generalization of models, and the inscrutability of many AI systems remain, and the use of these systems in clinical practice needs to be improved. The paper emphasizes the need for interdisciplinary collaboration, diverse dataset collection, and lightweight and interpretable AI models to solve these issues. Lastly, it brings together important findings and identifies research gaps, showing AI's potential to change the dermatology world for all patients.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2024</year>
    </publication_date>
    <pages>
     <first_page>16</first_page>
     <last_page>24</last_page>
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     <item_number item_number_type="article-number">3407</item_number>
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