MUMBAI, India, Aug. 12 -- Intellectual Property India has published a patent application (202611070218 A) filed by Ims Engineering College, Ghaziabad on June 04, 2026, for An Intelligent Multi-Modal Artificial Intelligence System And Method For Automated Medical Diagnosis And Clinical Decision Support.
Inventors include Dr. Siddharth Vats; Dr. Indu Bhatt; Miss Vandna Tomar; and Mr. Manoj Kumar Chaursia.
The application for the patent was published on August 07, 2026, under issue no. 32/2026.
Abstract: The present invention discloses an intelligent multi-modal artificial intelligence system and method for automated medical diagnosis and clinical decision support using integrated heterogeneous healthcare data obtained from multiple clinical modalities. The invention comprises a healthcare data acquisition module configured to collect medical imaging data, laboratory reports, physiological sensor readings, wearable healthcare device outputs, genomic information, electronic health records, and patient symptom profiles from distributed healthcare environments. The collected data is processed through a preprocessing and normalization engine configured to perform image enhancement, signal denoising, feature extraction, semantic analysis, and data standardization for downstream artificial intelligence analysis. A multi-modal data fusion framework utilizing deep learning architectures including convolutional neural networks, recurrent neural networks, transformer-based models, graph neural networks, and attention mechanisms integrates the processed healthcare information into a unified patient-centric representation model. An artificial intelligence-based diagnostic inference engine analyzes the integrated data to identify diseases, predict abnormalities, estimate disease progression, generate diagnostic confidence scores, and provide personalized clinical recommendations. The invention further comprises an explainable artificial intelligence module configured to generate interpretable diagnostic outputs including highlighted pathological regions, biomarker relationships, symptom correlations, and reasoning pathways for improved physician trust and clinical transparency. An adaptive learning subsystem continuously retrains predictive models using physician feedback, treatment outcomes, and updated clinical datasets to enhance diagnostic accuracy and system adaptability. The invention additionally supports secure cloud-edge healthcare deployment, federated learning, encrypted medical data transmission, and real-time clinical analytics for scalable implementation across hospitals, telemedicine systems, remote healthcare facilities, and intelligent healthcare infrastructures. Accompanied Drawing [FIGS. 1-2]
Disclaimer: Curated by HT Syndication.
