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  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher-id">JPSIM</journal-id>
      <journal-title-group>
        <journal-title>Journal of Pakistan Society of Internal Medicine</journal-title>
        <abbrev-title>J Pak Soc Intern Med</abbrev-title>
      </journal-title-group>
      <issn publication-format="print">2709-8729</issn>
      <issn publication-format="electronic">2710-0804</issn>
      <publisher>
        <publisher-name>Pakistan Society of Internal Medicine</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.70302/jpsim.v5i4.2481</article-id>
      <article-id pub-id-type="publisher-id">jpsim.v5i4.2481</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Digital Health</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>AI-empowered Emergency Department Cutting Edge Digital Medicine</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author" corresp="yes">
          <name>
            <surname>Chughtai</surname>
            <given-names>Suhail</given-names>
          </name>
          <email>Director@Medicalcity.online</email>
          <xref ref-type="aff" rid="aff1"/>
          <xref ref-type="corresp" rid="cor1">*</xref>
        </contrib>
      </contrib-group>
      <aff id="aff1">
        <institution>Consultant Orthopaedic Surgeon (locum) &amp; Medico-Legal Examiner, London, UK Clinical Director, Orthopaedics, ML Professionals, UK</institution>
      </aff>
      <author-notes>
        <corresp id="cor1">*Correspondence: Suhail Chughtai (Director@Medicalcity.online)</corresp>
      </author-notes>
      <pub-date date-type="pub" publication-format="electronic" iso-8601-date="2024-12-09">
        <day>09</day>
        <month>12</month>
        <year>2024</year>
      </pub-date>
      <pub-date date-type="collection" publication-format="electronic">
        <year>2024</year>
      </pub-date>
      <volume>5</volume>
      <issue>4</issue>
      <fpage>758</fpage>
      <lpage>761</lpage>
      <history>
        <date date-type="received" iso-8601-date="2024-11-04">
          <day>04</day>
          <month>11</month>
          <year>2024</year>
        </date>
        <date date-type="accepted" iso-8601-date="2024-11-15">
          <day>15</day>
          <month>11</month>
          <year>2024</year>
        </date>
      </history>
      <permissions>
        <copyright-statement>Copyright © 2024 The Author(s)</copyright-statement>
        <copyright-year>2024</copyright-year>
        <copyright-holder>The Author(s)</copyright-holder>
        <ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/"/>
        <license license-type="open-access" xlink:href="https://creativecommons.org/licenses/by/4.0/">
          <ali:license_ref xmlns:ali="http://www.niso.org/schemas/ali/1.0/">https://creativecommons.org/licenses/by/4.0/</ali:license_ref>
          <license-p>This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p>
        </license>
      </permissions>
      <self-uri xlink:href="https://psimj.com/article/jpsim.v5i4.2481" content-type="text/html"/>
      <self-uri xlink:href="https://psimj.com/article/jpsim.v5i4.2481.pdf" content-type="application/pdf"/>
      <abstract>
        <p>Artificial Intelligence (AI) is rapidly transforming emergency medicine, offering innovative solutions to longstanding challenges in both clinical and operational domains. The author examines the future potential of AI applications in emergency departments (EDs) with a view to enhancing critical medical functions, including automated triage systems, diagnostic support algorithms, and predictive analytics for patient deterioration. The author also explores AI's impact on administrative efficiency through workflow optimization, clinical documentation automation, and resource management. Through multiple case studies, the author demonstrates successful AI implementations in emergency care settings, while critically examining implementation challenges, including data security concerns and staff training requirements. This article provides a framework for healthcare administrators and clinicians to evaluate and implement AI solutions in emergency care settings, while highlighting areas requiring further research and development.</p>
      </abstract>
    </article-meta>
  </front>
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</article>
