Enhancing Privacy of Clinical Decision Support Systems with Federated Learning

Abstract

In the era of digital healthcare, Clinical Decision Support Systems (CDSS) have emerged as crucial tools for assisting healthcare professionals in making informed decisions by providing evidence-based insights at the point of care. These systems integrate medical expertise, scientific literature, and various empirical data to form a comprehensive knowledge base. Furthermore, CDSS increasingly leverage advanced Artificial Intelligence (AI) techniques to provide healthcare professionals with deeper, actionable insights. Despite their potential, AI approaches to CDSS face significant challenges, including maintaining patient privacy, access to vast, diverse data sources, and the need for adaptive learning mechanisms that can evolve with new clinical findings and patient data. This paper explores the integration of Federated Learning (FL) within CDSS to address these pressing concerns. We propose a concept that fosters collaborative and secure data analysis across multiple healthcare entities, involving model's training that respects patient privacy and complies with regulatory requirements, ultimately aiming to improve healthcare outcomes through advanced data analytics and more informed decision-making processes.