Blockchain-Enabled Explainable AI for Trusted Healthcare Systems

Transparency in AI decisions is critical for clinical adoption: The framework emphasizes that healthcare AI systems must provide explainable reasoning for their recommendations, not just predictions. This addresses a key barrier to doctors and patients trusting AI-assisted diagnoses and treatment decisions.

Blockchain creates an auditable record of both data and decisions: By combining blockchain with AI explanations, the system enables healthcare providers to trace where patient information came from, how it was used, and why specific clinical recommendations were made—supporting accountability and regulatory compliance.

Privacy and collaboration can coexist through federated architecture: The paper demonstrates that institutions can collaborate on research and share insights across borders while protecting individual patient privacy through edge-cloud computing, potentially accelerating medical discoveries without compromising data security.

Multiple layers of trust are necessary for responsible healthcare AI: The framework addresses both "data-level trust" (ensuring records aren't tampered with) and "decision-level trust" (ensuring AI reasoning is sound and clinically appropriate), recognizing that responsible AI requires security, interpretability, and clinical alignment working together.

Real-world applications include high-stakes scenarios: The use cases mentioned—rare disease detection and high-risk intervention decisions—highlight that this approach targets situations where AI errors have serious consequences, making transparency and auditability especially important for patient safety.