SYSTEMATIC REVIEW article
Front. Blockchain
Sec. Blockchain in Industry
This article is part of the Research TopicIndustrial Transformation through Blockchain: From Smart Manufacturing to Secure HealthcareView all 9 articles
BLOCKCHAIN MEETS AI IN HEALTHCARE:A REVIEW OF CONVERGENT TECHNOLOGIES FOR DIGITAL HEALTH TRANSFORMATION
Provisionally accepted
Jialin Liu1
Xiaowen Hu2*- 1King's College London Faculty of Life Sciences & Medicine, London, United Kingdom
- 2Dalian University of Technology, Dalian, China
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Blockchain and AI (Artificial Intelligence) are shaping a new medical ecosystem as two key technologies for the digital transformation of healthcare. This study discusses the digital health transformation path of blockchain and AI in the healthcare field from dimensions such as theme clustering, trend evolution, technical background, application, and future challenges. Blockchain has advantages in building a trusted data infrastructure. Artificial intelligence has a natural talent for enhancing the efficiency of clinical decision-making and operations. The deep integration between the two is driving the transformation of healthcare from single-point digitalization to system intelligence. Despite challenges such as regulatory systems, cross-institutional collaboration, talent structure, and medical ethics, reliable intelligent healthcare systems have become the development direction of future healthcare models. This paper provides systematic review and theoretical framework for understanding the evolutionary logic, key themes and future trends of blockchain-artificial intelligence integration.
Keywords: AI (artificial intelligence), Blockchain, clinical decision making, Digital Health Transformation, healthcare
Received: 12 Dec 2025; Accepted: 23 Jan 2026.
Copyright: © 2026 Liu and Hu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Xiaowen Hu
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