Blockchain Technology in Digital Health and Medical Technologies
Bibliographic record
Abstract
The rapid evolution of digital health technologies has created an urgent need for secure, transparent, and interoperable data management systems. The core problem addressed in this study is the fragmentation of healthcare data and the lack of trust among stakeholders in existing digital health infrastructures. The main goal is to examine how blockchain technology can drive digital health transformation through decentralized data governance and integration with other emerging technologies. To achieve this, the research employs a mixed bibliometric and systematic review methodology, analyzing peer-reviewed publications indexed in the Web of Science and comparing topic hierarchies with outputs from Google Scholar between 2017 and 2024. Using keyword co-occurrence and thematic mapping, six major domains were identified: genomics and precision medicine, telemedicine and mobile health, immersive technologies such as augmented and virtual reality, the Internet of Things and Health 5.0 systems, artificial intelligence and big data integration, and global and regional health management. The findings indicate that blockchain enhances healthcare by improving data security, ensuring traceability, facilitating interoperability across platforms, and enabling real-time data sharing in clinical and research environments. It also supports regulatory compliance and patient-centered data ownership. In conclusion, blockchain serves as a foundational technology for future digital health ecosystems, promoting transparency and decentralization across global health networks. This study contributes to the literature by offering a comprehensive framework for integrating blockchain with digital health innovations, providing valuable guidance for researchers, policymakers, and healthcare technologists.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".