perspectives and challenges of using artificial intelligence sensors in telemedicine:A Systematic Review
Code: G-1795
Authors: Mostafa Rajabzadeh ℗, Roghayeh Yazdani *, Maryam Yousefi, Zahra Yousefzadeh Jahromi, Sara Sharbatdar
Schedule: Not Scheduled!
Tag: Clinical Decision Support System
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Abstract:
Abstract
Introduction: Fueled by the Internet and IT, telemedicine is now a popular model, providing high-quality, accessible healthcare. AI promises further gains, aiding physicians in decision-making, automating tasks, and improving care delivery. However, AI adoption faces hurdles: tech limitations, system costs, constant updates, and the potential for reduced innovation. This review examines the benefits and challenges of AI integration in telemedicine. Methods: This study was conducted in accordance with the PICO criteria, in line with the research objective, and guided by the PRISMA checklist. A comprehensive search of articles from 2019 to 2025 was conducted in the databases PubMed, SCOPUS, CINAHL, Web of Science, SID, and Magiran, as well as the Google Scholar search engine. The search was conducted using the MESH keywords "artificial intelligence" "Telemedicine" "Digital Health" and using Boolean operators. Subsequently, two researchers independently reviewed and screened the retrieved articles based on the inclusion criteria. Results: After careful review of the inclusion and exclusion criteria and assessment of the quality of the articles, 11 articles out of a total of 315 articles were included in the initial search. Integrating artificial intelligence (AI) into telemedicine revolutionizes healthcare delivery, yielding significant improvements in patient outcomes and provider efficiency. Analysis across multiple studies reveals compelling quantitative evidence of AI’s positive impact. Remote patient monitoring (RPM) systems incorporating AI-powered devices have demonstrated a remarkable 25% reduction in hospital readmission rates (p0.05), indicating a statistically significant improvement in patient management and a reduction in healthcare costs associated with readmissions. This is complemented by a 15% increase in diagnostic accuracy (p0.01) achieved through AI-driven tools, exceeding the capabilities of traditional methods and leading to more accurate and timely diagnoses. Conclusion: The integration of AI and telemedicine holds transformative potential for healthcare by 2050, improving access, diagnosis, and efficiency. However, addressing legal, ethical, technological, and regulatory challenges, including data privacy, biases, and infrastructure needs, is crucial. Realizing AI’s full potential requires physician-led implementation, adherence to clinical practices, and robust evaluation frameworks, ensuring a more inclusive, efficient, and high-quality healthcare system, especially in remote areas.
Keywords
Artificial Intelligence, Telemedicine, Digital Health
