Healthcare is undergoing rapid digital transformation, with artificial intelligence, digital twins, federated learning, and blockchain increasingly used across diagnostics, monitoring, planning, and data governance. This study aims to critically evaluate emerging technologies in healthcare, identifying where they are applied across clinical care pathways, assessing their reported effectiveness, and analysing associated implementation challenges. A systematic review was conducted in accordance with PRISMA guidelines. Structured searches were performed in Scopus, IEEE Xplore, ScienceDirect, and Google Scholar for peer-reviewed studies published between 2020 and 2025. Search terms included combinations of healthcare technologies, digital health, telemedicine, Internet of Medical Things, artificial intelligence, machine learning, deep learning, digital twins, federated learning, and blockchain. Of 861 records identified, 90 studies met the inclusion criteria, and methodological quality was appraised using the Mixed Mehods Appraisal Tool (MMAT) to support interpretation of findings. It can be observed that most studies focus on image-based artificial intelligence with strong diagnostic performance. Digital twins are used for personalised simulation and risk prediction, while federated learning and blockchain support privacy-preserving training and secure data sharing. However, common limitations across the studies include single-centre datasets, limited external validation, heterogeneity in reporting, and constraints on integration and computation. Future directions cluster around evidence strengthening, standards-based interoperability, privacy-by-design governance, and implementation strategies to translate innovations into safe, effective clinical systems.
Ramkhelawan et al. (Thu,) studied this question.