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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.v5i2.2441</article-id>
      <article-id pub-id-type="publisher-id">jpsim.v5i2.2441</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Digital Health</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Artificial Intelligence's Transformative Role in Management of Electronic Medical Records</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 Microsoft Certified Specialist &amp; Telehealth/AI Software Designer</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="2024-06-07">
        <day>07</day>
        <month>06</month>
        <year>2024</year>
      </pub-date>
      <pub-date date-type="collection" publication-format="electronic">
        <year>2024</year>
      </pub-date>
      <volume>5</volume>
      <issue>2</issue>
      <fpage>553</fpage>
      <lpage>558</lpage>
      <history>
        <date date-type="received" iso-8601-date="2024-01-16">
          <day>16</day>
          <month>01</month>
          <year>2024</year>
        </date>
        <date date-type="accepted" iso-8601-date="2024-04-15">
          <day>15</day>
          <month>04</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.v5i2.2441" content-type="text/html"/>
      <self-uri xlink:href="https://psimj.com/article/jpsim.v5i2.2441.pdf" content-type="application/pdf"/>
      <abstract>
        <p>The integration of ChatGPT's powerful document analysis capabilities into healthcare has the potential to transform the way medical professionals and researchers extract and utilize information from electronic medical records (EMRs) and biomedical literature. By leveraging ChatGPT's advanced natural language processing and machine learning techniques, healthcare providers can efficiently identify and extract both objective and subjective information from patient records, enabling them to make more informed decisions, provide personalized care, and ultimately improve patient outcomes. In the realm of medical research, ChatGPT can streamline the process of conducting systematic literature reviews, facilitate the analysis of large clinical datasets, and aid in the generation of novel hypotheses by uncovering hidden patterns and connections across multiple domains of biomedical knowledge. However, as healthcare organizations increasingly adopt ChatGPT, protecting patient privacy and ensuring the confidentiality of sensitive medical information becomes a critical concern. The use of document anonymizers plays a vital role in safeguarding patient data by removing personally identifiable information from medical records before they are processed by ChatGPT. By striking a balance between leveraging the power of artificial intelligence and maintaining the highest standards of data privacy and security, the healthcare industry can harness the full potential of ChatGPT to enhance the quality and efficiency of care delivery, accelerate medical research, and drive meaningful improvements in patient outcomes.</p>
      </abstract>
    </article-meta>
  </front>
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