Digital technologies supporting predictive healthcare: Review of research trends

Abstract

This study examines research trends in digital technologies supporting predictive healthcare, with particular attention to the role of digital twins. A structured bibliometric analysis combined with qualitative thematic analysis was conducted using publications indexed in the Scopus and Web of Science databases from 2015 to 2025. The results indicate a clear shift towards integrated, data-driven healthcare solutions, in which digital twins function as central frameworks linking artificial intelligence, machine learning and Internet of Medical Things technologies. Three emerging thematic areas were identified: integrated patient data ecosystems, predictive and preventive digital twins, and digital twin–based treatment planning and patient response simulation. The findings highlight increasing interest in personalised, predictive and simulation-oriented healthcare models. At the same time, the analysis reveals a gap between technological development and routine clinical implementation. The study contributes to a clearer understanding of the evolving structure of this research field and outlines directions for future research and application in predictive healthcare.

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