Ethics, Privacy, and Security Challenges in AI and Blockchain-Driven Digital Health Ecosystems

Abstract

This article discusses how integrating AI and blockchain technology into digital health platforms might help and hurt privacy, fairness, transparency, and compliance. This research compares AI and blockchain technologies for making honest and ethical healthcare choices. We are investigating federated learning, homomorphic encryption, differential privacy, zero-knowledge proofs, self-sovereign identity systems, explainable AI, blockchain interface protocols, and privacypreserving AI systems. We rated each technique based on data protection, ethical data collecting, computer justice, openness, and system security. While most approaches perform well in certain locations, they all have issues that may render them unsuitable for use in healthcare. The proposed solution addresses these concerns and outperforms speed standards. It evaluates ethical risks based on bias, fairness, and transparency and is continuously improving ethical decision-making. These evaluations improve healthcare AI systems' reliability, fairness, and clarity during decision-making. This implies its potential application in AI-driven healthcare systems.