Deep Learning and LLM-Integrated Framework for Real-Time Decision Support in Healthcare

Abstract

In the field of healthcare, real-time AI-based decision support is vital because multimodal clinical data are fast growing, the cases of complicated patients, and the possibility to provide instant and understandable recommendations is in demand. Developments in deep learning and intelligent thinking will allow revolutionary clinical intelligence that will be able to provide medical support proactively and accurately. The framework presented in this paper is a Deep Learning and an LLM-based combination that will continually work under the medical images, physiological data, formatted EHR data and clinical text. The model uses attention-directed multimodal fusion, predictive inference, and uncertainty estimation and an LLM-based reasoning core in line with medical knowledge to provide interpretable and contextual clinical directions. Federated architecture guarantees collaborative learning that is privacy-offering and sensitive data are not revealed. Relative experimental performance proves that the proposed framework outperforms Knowledge-Graph Systems, CNN-Based Models, Transformer Healthcare Reasoning Systems, and Hybrid AI Frameworks significantly with a 0.98 accuracy, 0.97 precision, 0.96 recall, 0.99 AUC, anomalously low 0.03 uncertainty and latency of 140 ms. In general, the framework provides a very dependable, secure, explainable as well as responsive platform of next generation real time clinical decision support.