Purpose:
Accurate disease diagnosis and personalized rehabilitation planning are among the major priorities of modern medicine. However, challenges related to data security, the integration of patient records, and the secure exchange of information among healthcare institutions remain significant concerns. This study aimed to examine the synergy between artificial intelligence (AI) and distributed ledger technology (DLT) in improving disease diagnosis and rehabilitation processes, with an emphasis on the potential of these technologies to enhance the accuracy and efficiency of clinical data analysis, facilitate the management and exchange of health data, and strengthen privacy protection mechanisms.
Methods:
This review study was conducted to identify relevant literature through searches of PubMed, Scopus, Web of Science, and IEEE Xplore for the period from 2023 to the date of the last search in 2026. The search strategy incorporated combinations of keywords related to artificial intelligence, machine learning (ML), deep learning (DL), DLT, blockchain, disease diagnosis, and rehabilitation. Studies investigating the applications of AI algorithms in disease diagnosis and prediction and the assessment of patient recovery, as well as studies examining the role of DLT in the management, storage, and exchange of health data, were selected according to predefined inclusion and exclusion criteria. Following duplicate removal, preliminary screening, and full-text assessment, 15 articles were selected for qualitative analysis. In addition, the roles of technologies such as federated learning and smart contracts in data security and governance frameworks were examined as key components of this technological synergy.
Results:
The findings from the selected studies indicated that AI has considerable potential for analyzing clinical data, supporting disease diagnosis, and predicting patient recovery trajectories. In contrast, DLT can contribute to improving the integrity and secure management of health data through traceable records of data-related events, access management, and the facilitation of information exchange among healthcare institutions. Furthermore, integrating these technologies with approaches such as federated learning and smart contracts may facilitate the development of intelligent systems for data exchange and the delivery of remote diagnostic and rehabilitation services. However, the realization of these capabilities depends on data quality, system architecture, privacy protection requirements, and performance validation in clinical settings.
Conclusion:
The convergence of AI and DLT offers a promising approach to developing intelligent systems for disease diagnosis and rehabilitation and improving health data management. Nevertheless, challenges related to scalability, implementation costs, system interoperability, legal considerations, and privacy protection remain to be addressed. Effective implementation of this technological synergy therefore requires standardized frameworks for data governance, security assessment, and clinical validation. Further empirical evaluation of these solutions in real-world healthcare settings is also necessary to determine their effectiveness, feasibility, and clinical implications.