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.
Rajendran Renganathan, P. Vats, S. P et al.· 2026 International Conferenc...· 0 citations
The growing use of interconnected and digital systems in clinical settings has increased the necessity of smart and robust protection systems that can assume extremely rigid privacy and reliability requirements. This paper presents the GuardianMesh: Anomaly-Resilient Federated Orchestration (GM-ARFO) a new AI-based threat prevention model that can be used to provide security to the world of distributed healthcare information ecosystems. The method proposed will allows collaborative intelligence between heterogeneous medical nodes and does not present sensitive patient information or centralised control. GuardianMesh (GM) works by using local clinical and system cues to create compact privacy preserving representations in the edge and then a detection of anomalous behaviors is possible early on. These depictions are jointly trained in an effective federated orchestration system that is resilient to adversarial manipulation and communication inefficiently. A dec-layer adjudication layer is what is used to package distributed evidence of anomalies to facilitate swift and automatic response procedures with have minimum impact to clinical processes. Moreover, adaptive monitoring adapts to behavior drift and the changing attack plans all the time, ensuring the reliability of detection over a long period. Thorough tests in various conditions of operation and adversary show that GM-ARFO has a high level of detection, low false alarms, lower response time in addition to maintaining data confidentiality. The findings support the fact that the suggested GuardianMesh framework offers a scalable, future-restaurant, and privacy-aware platform of ensuring the safety of next-generation healthcare information infrastructures. The suggested method attains an overall detection accuracy of 96.8%, indicating a highly dependable identification of anomalous behaviours in remote healthcare systems.
Ramgopal Kashyap, Vrince Vimal, Vikalp Sharma et al.· 2026 International Conferenc...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
