Enhancing data privacy in healthcare with deep learning models & AI personalization techniques

Abstract

Artificial Intelligence (AI) has great potential to transform healthcare but the research to clinical practice transition has been gradual. Differences in medical record formats, lack of accessible data for training AI systems, and limitations imposed by privacy regulations and present challenges to population wide AI integration. This requires new approaches to data sharing that preserve privacy, privacy that is very en vogue today enabled social innovation for developing AI driven healthcare applications. This study presents a scoping review of state of the art strategies that have been proposed to protect patient privacy during the preparation stage for progression of AI in healthcare with a emphasis on technologies including Federated Learning and Hybrid techniques. It further discusses possible privacy attacks, security issues, and future directions of this emerging domain.AI in Healthcare AI has been considered one of the most transformative technologies in healthcare, having the potential to solve some of the complex medical problems. The use of AI with machine learning or deep learning can help in diagnosis, treatment select, and monitor patients for more accurate and efficient healthcare.