TELEMEDICINE 2027 Open access

Apps and artificial intelligence in telemedicine and telerehabilitation: state of the art and perspectives

G. Loffredo,1,2 S. Tagliaferri2 | 1Department of Mathematics and Physics, University of Campania ”Luigi Vanvitelli”, Naples; 2Kelyon S.r.l., Naples, Italy

Authors

Published
28 January 2026
Section
TELEMEDICINE 2027
DOI
10.4081/gimle.784

Abstract

The integration of artificial intelligence (AI) into medicine has profoundly transformed telemedicine, reshaping the delivery of healthcare services toward more accessible, personalized, and data-driven models.1-3 AI-enhanced telemedicine systems support rapid patient monitoring, automated triage, and informed clinical decision-making, thereby improving efficiency and continuity of care, especially in resource-limited settings.3,4 In telerehabilitation, AI-driven approaches leveraging machine learning, deep learning, and advanced data analytics enable the development of adaptive, patient-specific rehabilitation programs delivered through wearable sensors and immersive technologies such as virtual reality (VR). Current evidence indicates that smartphone-based rehabilitation platforms5 and AI-supported remote monitoring systems can achieve clinical outcomes comparable to conventional in-person rehabilitation, while enhancing treatment adherence, patient engagement, and cost-effectiveness. These benefits are particularly relevant for chronic conditions and aging populations, where continuity of care and long-term monitoring are essential. Moreover, AI-based tools facilitate continuous assessment of functional recovery through real-time analysis of physiological signals, movement patterns, and patient-reported outcomes, enabling the dynamic adjustment of therapeutic protocols. Despite these advancements, several challenges remain. Socioeconomic disparities continue to influence access to digital health technologies, potentially limiting the widespread adoption of AI-driven telemedicine and telerehabilitation solutions.6 Additional concerns include data privacy, algorithm transparency, regulatory frameworks, and the need for robust clinical validation to ensure safety, equity, and trust in AI-enabled healthcare systems.7 Looking ahead, future perspectives suggest a progressive shift toward increasingly intelligent, interoperable, and patient-centered digital ecosystems. The convergence of AI with mobile applications and virtual environments is expected to further advance personalized rehabilitation pathways and predictive healthcare models, ultimately supporting more proactive, efficient, and equitable delivery of care across diverse clinical settings.

Keywords

References

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