A Sustainable and Adaptive Hybrid AI-Based Approach for Clinical Decision Support and Healthcare Analytics

The exponential growth of multimodal healthcare data as Biomedical literature, medical notes, clinical text, and electronic healthcare records, has enhance the need for intelligent and adaptive healthcare analytics systems. Traditional clinical decision support systems mostly rely on static and single-modal methods, that restrict the contextual understanding, explainability, and real-time clinical intelligence. This proposed model presents a sustainable and adaptive Hybrid AI-Based Framework for Clinical Decision Support and Healthcare Analytics integrating multimodal healthcare data through transformer-based architectures, Explainable AI techniques, and biomedical language models. The proposed framework is amalgamation of Vision Transformer (ViT), BioBERT/ClinicalBERT, multimodal feature fusion, also attention-based explainability mechanisms to boost biomedical summarization, disease prediction, and intelligent clinical reasoning. The system aims to provide scalable, adaptive, interpretable, and real-time healthcare intelligence while reducing clinician workload and improving diagnostic efficiency. The proposed approach is expected to enhance prediction accuracy, semantic understanding, and trustworthy healthcare analytics, contributing toward next-generation intelligent Clinical Decision Support Systems (CDSS).