Privacy-Preserving AI for Medical Applications: A Scalable and Secure Approach

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Author(s): Usharani Bhimavarapu (Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, India)
Copyright: 2025
Pages: 24
Source title: Strategic Management, Regulatory Challenges, and Global Governance of MedTech
Source Author(s)/Editor(s): Ricardo Marcão (ISLA Santarem, Polytechnic University, Portugal & NECE, University of Beira Interior, Portugal ), Vasco Ribeiro Santos (Instituto Politécnico de Tomar, Portugal)and Stéphanie Monteiro (Institute for Systems and Robotics, Portugal)
DOI: 10.4018/979-8-3373-1205-7.ch006

Keywords: Business & Management / Management Science / Medical Information Science Reference / Medical Technologies

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Abstract

The sudden infusion of artificial intelligence (AI) into the medical sector requires strong frameworks that address regulatory requirements, ethical issues, privacy issues, and concerns related to interoperability. The current research conceptualizes a dynamic system of AI governance that changes dynamically along with advancements in AI, avoiding the constraints of fixed models of governance. In contrast to conventional methods, the model incorporates adaptive compliance processes, explainable AI models, and user-managed data-sharing systems to enable transparency and trustworthiness. It also uses federated learning methods to enable secure and scalable AI adoption in healthcare and avoid data heterogeneity challenges. The model includes enhanced privacy protection with less homomorphic encryption and optimally utilized blockchain, reducing computation overhead and allowing practical application.

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Privacy-Preserving AI for Medical Applications: A Scalable and Secure Approach

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