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<article xlink="http://www.w3.org/1999/xlink" dtd-version="1.0" article-type="general-medicine" lang="en">
  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher">JOHS</journal-id>
      <journal-id journal-id-type="nlm-ta">Journ of Health Scien</journal-id>
      <journal-title-group>
        <journal-title>Journal of HealthCare Sciences</journal-title>
        <abbrev-journal-title abbrev-type="pubmed">Journ of Health Scien</abbrev-journal-title>
      </journal-title-group>
      <issn pub-type="ppub">2231-2196</issn>
      <issn pub-type="opub">0975-5241</issn>
      <publisher>
        <publisher-name>Radiance Research Academy</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="publisher-id">362</article-id>
      <article-id pub-id-type="doi">http://dx.doi.org/10.52533/JOHS.2024.41241</article-id>
      <article-id pub-id-type="doi-url"/>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>General Medicine</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Ethical Obligations and Patient Consent in the Integration of Artificial Intelligence in Clinical Decision-Making&#13;
</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Albalawi</surname>
            <given-names>Anwar Fahad</given-names>
          </name>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Yassen</surname>
            <given-names>Mohammad Hamzah</given-names>
          </name>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Almuraydhi</surname>
            <given-names>Khaled Mohammed</given-names>
          </name>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Althobaiti</surname>
            <given-names>Ahmed Dhaifallah</given-names>
          </name>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Alzahrani</surname>
            <given-names>Hadeel Hassan</given-names>
          </name>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Alqahtani</surname>
            <given-names>Khalid Mohammad</given-names>
          </name>
        </contrib>
      </contrib-group>
      <pub-date pub-type="ppub">
        <day>30</day>
        <month>12</month>
        <year>2024</year>
      </pub-date>
      <volume>4</volume>
      <issue>12</issue>
      <fpage>957</fpage>
      <lpage>963</lpage>
      <permissions>
        <copyright-statement>This article is copyright of Popeye Publishing, 2009</copyright-statement>
        <copyright-year>2009</copyright-year>
        <license license-type="open-access" href="http://creativecommons.org/licenses/by/4.0/">
          <license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0) Licence. You may share and adapt the material, but must give appropriate credit to the source, provide a link to the licence, and indicate if changes were made.</license-p>
        </license>
      </permissions>
      <abstract>
        <p>Artificial intelligence (AI) is transforming clinical decision-making by enhancing diagnostic accuracy, treatment planning, and patient management. However, its integration into healthcare raises ethical challenges, particularly regarding informed consent, transparency, accountability, and patient privacy. Traditional consent models face limitations as AI systems often operate as __doublequotosingblack boxes,__doublequotosing making their processes difficult to understand. This complexity necessitates the development of explainable AI (XAI) frameworks and dynamic consent models that ensure patients comprehend how their data is used and how decisions are made. Transparency in algorithmic design and decision-making processes is critical for building trust among patients and clinicians. AI algorithms must also be accountable for their recommendations, with clear guidelines to address potential errors, biases, and adverse outcomes. Collaborative efforts between developers, healthcare providers, and regulators are essential to establish ethical and legal standards for the responsible use of AI in clinical settings. Ensuring data security and patient privacy is another critical consideration, as AI systems rely on large datasets, often containing sensitive health information. Techniques like encryption, anonymization, and federated learning offer promising solutions to safeguard data while maintaining its utility for AI training and implementation. Additionally, the risk of algorithmic bias underscores the need for diverse datasets and rigorous validation of AI tools to prevent healthcare disparities. Ethical governance must address the balance between advancing medical innovation and protecting individual rights. The adoption of privacy-preserving technologies, robust security measures, and culturally sensitive consent practices can further enhance ethical compliance. By prioritizing these aspects, AI has the potential to improve healthcare delivery while upholding patient autonomy and trust. Addressing these challenges through interdisciplinary collaboration ensures that AI integration aligns with ethical principles and supports equitable, effective, and transparent healthcare systems.&#13;
</p>
      </abstract>
      <kwd-group>
        <kwd>artificial intelligence</kwd>
        <kwd> informed consent</kwd>
        <kwd> patient privacy</kwd>
        <kwd> healthcare ethics</kwd>
        <kwd> algorithmic transparency</kwd>
      </kwd-group>
    </article-meta>
  </front>
</article>