AI-Induced Cybersecurity Risks in Healthcare: A Narrative Review of Blockchain-Based Solutions Within a Clinical Risk Management Framework

Gianmarco Di Palma,1,2 Roberto Scendoni,3 Davide Ferorelli,4 Anna De Benedictis,1,5 Vittoradolfo Tambone,2 Francesco De Micco1,2 1Operative Research Unit of Clinical Affairs, Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy; 2Research Unit of Bioethics and Humanities, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, Italy; 3Department of Law, Institute of Legal Medicine, University of Macerata, Macerata, Italy; 4Interdisciplinary Department of Medicine (DIM), Section of Legal Medicine, University of Bari “aldo Moro”, Bari, Italy; 5Research Unit of Nursing Science, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, ItalyCorrespondence: Gianmarco Di Palma, Operative Research Unit of Clinical Affairs, Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy, Email [email protected]/Objectives: Artificial intelligence (AI) is revolutionizing the healthcare industry, improving diagnoses, treatments, and clinical processes. However, its integration poses significant cybersecurity risks, including data breaches, algorithmic opacity, and vulnerabilities in AI-controlled medical devices. This narrative review analyzes these threats and evaluates blockchain technology as a potential mitigation strategy within a Clinical Risk Management framework.Methods: The literature search was conducted on PubMed, Scopus, and Web of Science, considering peer-reviewed publications from 2000 to January 2025. 1,204 articles were identified. Inclusion criteria included studies on cybersecurity risks in healthcare, blockchain applications in the clinical setting, and regulatory references (eg, General Data Protection Regulation). Conference abstracts, non-English articles, and non-peer-reviewed contributions were excluded. To ensure methodological rigor, the Scale for the Assessment of Narrative Review Articles criteria were applied.Results: The thematic analysis highlighted recurring critical issues: difficulties with informed consent, unauthorized access to sensitive data, and systemic vulnerabilities in hospital digital infrastructures. Blockchain presents a promising solution thanks to its decentralization, immutability, and transparency. Integration with smart contracts enables dynamic consent management, secure data sharing, and real-time monitoring of medical devices. Permissioned networks improve traceability and regulatory compliance, while Layer 2 solutions and optimized consent protocols address scalability challenges.Conclusion: Despite its potential, blockchain adoption faces obstacles: high costs, regulatory rigidity, and poor acceptance among healthcare professionals. The review highlights the need for pilot projects, interdisciplinary collaboration, and regulatory updates for effective integration. Combining AI and blockchain in Clinical Risk Management can transform clinical risk management from reactive to proactive, improving patient safety, data governance, and accountability.Keywords: clinical risk management, artificial intelligence, blockchain, cybersecurity, healthcare, patient safety