<![CDATA[The development of telemedicine and Remote Patient Monitoring (RPM) is increasing along with the need for efficient, adaptive, and data-driven remote healthcare services. Artificial Intelligence (AI) plays a crucial role in strengthening these systems through predictive analysis, medical classification, and real-time patient monitoring. However, research on AI integration in telemedicine and RPM remains scattered and exhibits wide methodological variation, necessitating a systematic review to understand the consistency of findings and the direction of research development. This study conducted a Systematic Literature Review (SLR) following the PRISMA 2020 protocol, analyzing 128 publications from 2020–2025 obtained from Scopus, PubMed, IEEE Xplore, and Google Scholar. This study combined SLR synthesis with bibliometric mapping (co-occurrence and thematic mapping) to highlight the evolution of themes and topical interrelationships more explicitly. Bibliometric analysis results show an increase in the number of publications from 12 articles in 2020 to 45 articles in 2024, a nearly fourfold increase, before stabilizing in 2025. Co-occurrence and thematic mapping findings reveal four main themes: telemedicine–AI, computational methods based on machine learning and deep learning, physiological monitoring, and human factors in clinical evaluation. The study also identifies several challenges, including data security, signal quality, model transparency, and healthcare worker readiness. Theoretically, the findings emphasize that AI integration in telemedicine–RPM needs to be understood as a socio-technical issue that demands human-centered evaluation. Policy-wise, strengthening data governance and clinical validation standards is necessary for more accountable and secure implementation. This study concludes that AI plays a central role in the development of telemedicine and RPM, but further studies are needed on service personalization, multimodal data integration, and large-scale clinical validation.]]>
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