Artificial Intelligence Techniques in Blockchain-Integrated Healthcare and Financial Systems: A Systematic Review and Future Research Agenda

Abstract

<title>Abstract</title> Blockchain and artificial intelligence (AI) are two innovative technologies that are impacting modern infrastructures with resilience, intelligence, and transparency. Blockchain offers immutability, decentralization, and trust, while AI facilitates decision assistance, anomaly detection, and predictive analytics. Scholarly interest in their convergence has grown for mission-critical domains including healthcare and finance. This narrative review evaluates the possibilities, difficulties, and future prospects of combining blockchain technology with artificial intelligence by compiling 53 research papers published between 2018 and 2025. This evaluation categorizes AI methods used in blockchain-integrated systems, including explainable AI models, federated learning, deep learning (CNN, RNN, transformers), and supervised learning (SVM, RF). Using PRISMA-inspired narrative synthesis principles, this work systematically synthesizes 53 peer-reviewed papers to ensure methodological openness and analytical rigor. The healthcare sector makes extensive use of telemedicine, secure medical data transmission, diagnostic support, and drug supply chain traceability. Fraud detection, digital banking innovation, and the creation of central bank digital currencies are all made possible by the integration of blockchain and artificial intelligence in financial institutions. The review creates a taxonomy of AI techniques and assesses their relative effectiveness and drawbacks in the financial and healthcare sectors. The analysis reveals persistent issues like scalability, interoperability, privacy, and regulatory ambiguity in addition to ethical considerations concerning explainability and fairness. We point out shortcomings in scalable federated blockchain–AI architectures, XAI frameworks, and benchmarking datasets. This paper offers a thorough synthesis that gives scholars and business experts a comprehensive understanding of how blockchain and artificial intelligence will affect future infrastructures. The study offers a comparative assessment of methodological methods, identifies unresolved problems with explainable models and federated architectures, and suggests a methodical research plan in addition to mapping applications of blockchain–AI convergence. Unlike other evaluations that concentrated on a single domain, this analysis reveals shared design principles and governance implications by presenting a cross-sector synthesis of healthcare and finance systems.

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