A New Framework for Trustworthy AI in Personalized Medicine
A comprehensive review charts the convergence of deep federated learning and blockchain technology to create privacy-preserving AI systems for healthcare, with a specific focus on Ayurvedic medicine. The analysis of 87 papers reveals that while federated learning enables collaborative model training across institutions without sharing sensitive patient data, blockchain provides an immutable audit trail, fine-grained access control, and incentive mechanisms. The proposed future direction is a novel framework integrating deep learning with a Proof-of-Authority blockchain, designed to manage reputation and model gradients, aiming to deliver personalized, Prakriti-informed care while ensuring robust data privacy and regulatory compliance.
Study Significance: For machine learning practitioners, this review underscores the critical trade-offs in latency, energy use, and system complexity when integrating advanced privacy-preserving techniques like federated learning with blockchain. It provides a concrete roadmap for deploying trustworthy AI in data-sensitive domains, moving beyond theoretical models to address real-world challenges of data scarcity, model robustness, and auditable deployment. This work directly informs the development of next-generation ensemble methods and neural networks that are not only accurate but also ethically and legally sound.
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