<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE article PUBLIC "-//NLM//DTD Journal Publishing DTD v2.3 20070202//EN" "journalpublishing.dtd">
<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" article-type="research-article">
  <front>
    <journal-meta>
      <journal-id journal-id-type="nlm-ta">REA Press</journal-id>
      <journal-id journal-id-type="publisher-id">null</journal-id>
      <journal-title>REA Press</journal-title><issn pub-type="ppub">3042-1306</issn><issn pub-type="epub">3042-1306</issn><publisher>
      	<publisher-name>REA Press</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">https://doi.org/10.22105/thi.vi.33</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Research Article</subject>
        </subj-group>
        <subj-group><subject>Artificial intelligence, Machine learning, Digital health, Telemedicine, Remote patient monitoring</subject></subj-group>
      </article-categories>
      <title-group>
        <article-title>The role of artificial intelligence and machine learning in digital health and telemedicine ecosystems: A comprehensive review</article-title><subtitle>The role of artificial intelligence and machine learning in digital health and telemedicine ecosystems: A comprehensive review</subtitle></title-group>
      <contrib-group><contrib contrib-type="author">
	<name name-style="western">
	<surname>Ghanbari</surname>
		<given-names>Robab </given-names>
	</name>
	<aff>Faculty of Electrical and Computer Engineering, Noshirvani University of Technology, Babol, Iran.</aff>
	</contrib></contrib-group>		
      <pub-date pub-type="ppub">
        <month>03</month>
        <year>2026</year>
      </pub-date>
      <pub-date pub-type="epub">
        <day>09</day>
        <month>03</month>
        <year>2026</year>
      </pub-date>
      <volume>3</volume>
      <issue>1</issue>
      <permissions>
        <copyright-statement>© 2026 REA Press</copyright-statement>
        <copyright-year>2026</copyright-year>
        <license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/2.5/"><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.</p></license>
      </permissions>
      <related-article related-article-type="companion" vol="2" page="e235" id="RA1" ext-link-type="pmc">
			<article-title>The role of artificial intelligence and machine learning in digital health and telemedicine ecosystems: A comprehensive review</article-title>
      </related-article>
	  <abstract abstract-type="toc">
		<p>
			Artificial Intelligence (AI) and Machine Learning (ML) have become essential technological drivers in digital health and telemedicine, enabling more efficient clinical workflows, improved diagnostic accuracy, and optimized remote care delivery. With the growing adoption of Remote Patient Monitoring (RPM), Mobile Health (mHealth), wearable devices, and Internet of Medical Things (IoMT) ecosystems, AI-driven systems now play a crucial role in predictive analytics, personalized care, and intelligent clinical decision-making. This review provides an in-depth analysis of emerging AI/ML-powered digital health applications, technological advancements, clinical benefits, implementation challenges, and future opportunities. The paper synthesizes recent findings (2019–2025) from leading scientific literature and highlights the impact of AI on telehealth interventions, smart hospitals, digital therapeutics, virtual care, and Health Information Systems (HISs). It further explores interoperability issues, privacy and security concerns, blockchain-based healthcare models, and policy implications for the next generation of digital health ecosystems.
		</p>
		</abstract>
    </article-meta>
  </front>
  <body></body>
  <back>
    <ack>
      <p>null</p>
    </ack>
  </back>
</article>