Blockchain and explainable AI could transform clinical decision-making

A new framework that combines blockchain technology with explainable artificial intelligence could help overcome one of the most pressing barriers to digital healthcare: the lack of trust in both data security and AI-driven medical decisions. Md Talha Mohsin of the University of Tulsa has published a study that outlines a model designed to provide secure, transparent, and interpretable healthcare solutions capable of gaining acceptance from both clinicians and patients.

The article, “Blockchain-Enabled Explainable AI for Trusted Healthcare Systems,” published on arXiv, introduces the Blockchain-Integrated Explainable AI Framework (BXHF). This system is built to address two critical challenges: ensuring the safe exchange of sensitive medical information and improving the transparency of AI models that support clinical decision-making.

How does blockchain reinforce data security in healthcare?

The study highlights how patient trust in healthcare AI often collapses at the data level. Medical records are highly sensitive and vulnerable to breaches, manipulation, and unauthorized sharing. Mohsin argues that blockchain’s decentralized and immutable ledger provides a solution by guaranteeing that once patient data is recorded, it cannot be tampered with.

Within BXHF, blockchain is employed to log every transaction, ensuring a verifiable and auditable history of data access. Smart contracts enable consent-based sharing, allowing patients and institutions to set explicit rules on who can view or use medical records. Homomorphic encryption further secures patient information, ensuring that sensitive data can be analyzed without being exposed.

This design, according to the research, creates what Mohsin calls data-level trust. By embedding blockchain into the architecture of healthcare AI, the framework ensures tamper-proof and transparent data handling, which is crucial for regulatory compliance and public confidence.

Can explainable AI improve clinical decision-making?

While blockchain secures information, the other barrier to adoption lies in how AI generates predictions. Traditional machine learning and deep learning systems are often criticized as “black boxes,” producing accurate results but offering little insight into how those results were reached. In clinical environments, where decisions may involve life-or-death outcomes, lack of interpretability undermines trust.

BXHF incorporates explainable AI (XAI) techniques that provide clear reasoning behind AI outputs. Explanations are cryptographically bound and stored on blockchain, ensuring not only that decisions are transparent but also that their justifications are verifiable and permanent. This approach builds what the study defines as decision-level trust.

The integration of XAI is particularly significant for high-risk medical scenarios such as cancer treatment, organ transplants, or rare disease diagnostics. In these contexts, doctors must understand the logic behind AI recommendations before acting on them. Mohsin emphasizes that embedding explainability ensures AI is not just a tool for computation but a partner in clinical judgment.

What practical applications could this framework support?

The study outlines multiple scenarios where BXHF could be deployed to transform healthcare delivery. One key use case is in the diagnosis of rare diseases, where collaboration across institutions is often required. By enabling secure and explainable data sharing, hospitals could work together without risking patient confidentiality.

Another application lies in cross-border clinical research. With medical data subject to different regulatory standards across countries, blockchain-enabled consent mechanisms could provide a standardized, compliant pathway for sharing information internationally. BXHF could also be applied in federated learning, allowing multiple hospitals to train AI models collectively without moving raw data from their systems.

The framework’s architecture is based on a hybrid edge–cloud model, allowing for both scalability and local data protection. Edge devices manage immediate data processing within hospital systems, while cloud resources provide the computational power for advanced model training and explanation generation.

These features make the framework adaptable for a wide range of healthcare environments, from local clinics to international research collaborations.

Why this study matters for healthcare and policy

The research underscores that technical innovation alone is not enough to drive adoption of AI in healthcare. Trust, built through transparency and security, is the deciding factor. By addressing both the integrity of patient data and the interpretability of AI decisions, BXHF offers a holistic solution to the credibility problem facing digital health systems.

The study also highlights regulatory implications. Healthcare regulators demand both data protection and explainability in medical AI, but most current systems prioritize one over the other. The integration of blockchain and XAI provides a pathway to satisfy both requirements simultaneously. This could accelerate regulatory approval for AI-driven clinical tools while strengthening patient and provider confidence.

Policymakers are urged to view BXHF not only as a technical innovation but also as a governance model. Its design could inform standards for secure and explainable healthcare AI systems worldwide. By embedding trust into both data handling and decision-making, the framework aligns with global efforts to ensure responsible AI deployment in sensitive sectors.