Artificial Intelligence for Intelligent and Privacy-Preserving Healthcare: A Survey of Medical Image Analysis, Multimodal Diagnosis, Semi-Supervised Learning, and Personalized Healthcare Systems

Abstract

Artificial intelligence (AI) is increasingly embedded in healthcare, from image-based diagnosis to patient-facing management tools, yet the supporting literature is fragmented across communities that rarely read one another. Motivated by the need for a coherent view, this survey examines four recent peer-reviewed studies that together span multimodal COVID-19 diagnosis, privacy-preserving learning on encrypted medical images, graph-refined semi-supervised pathology classification, and an AI-enabled mobile application for personalized healthcare management and doctor recommendation. Using a structured narrative methodology, the reviewed studies are classified into a five-category taxonomy, compared along technical dimensions, healthcare capabilities, and reported limitations, and synthesized into a layered view that treats imaging, multimodal fusion, privacy preservation, label-efficient learning, and personalization as complementary layers of intelligent healthcare rather than independent technologies. The synthesis indicates that each study advances one layer under its own experimental setting, and that none addresses privacy, label efficiency, multimodality, explainability, and clinical validation together. Recurring gaps include limited clinical validation, dataset constraints, unquantified generalization across institutions, privacy–performance trade-offs, and inconsistent evaluation protocols that prevent direct numerical comparison. Future directions include privacy-preserving multimodal learning, reduced reliance on annotations, explainable and robust models, integration of imaging with clinical records, and multi-site clinical validation. This review is not exhaustive, and no new experiments are presented.