Artificial Intelligence (AI) is redefining modern healthcare by enabling predictive, preventive, personalized, and participatory (P4) medicine. While earlier research has demonstrated the feasibility of AI-driven disease diagnosis, advanced developments now focus on multimodal learning, federated learning, explainable AI (XAI), and real-time telemedicine integration. This paper presents an advanced framework for AI-driven healthcare that integrates heterogeneous data sources including Electronic Health Records (EHRs), medical imaging, genomic data, wearable sensor streams, and clinical narratives. The proposed research introduces a hybrid deep learning architecture combining Convolutional Neural Networks (CNNs), Transformer-based models, and Graph Neural Networks (GNNs) for multimodal predictive modeling. Additionally, privacy-preserving federated learning mechanisms are incorporated to address data security concerns while enabling collaborative model training across distributed hospitals. The study further integrates explainability frameworks such as SHAP and attention visualization to enhance clinician trust and regulatory compliance. Experimental validation using benchmark clinical datasets demonstrates improved predictive performance (AUC > 0.94) compared to traditional machine learning models. This research contributes a scalable, interpretable, and privacy-aware AI ecosystem capable of supporting disease diagnosis, risk stratification, and intelligent telemedicine delivery.
Shilwant et al. (Mon,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: