RPI ID: 2023-052-301
Innovation Summary: This technology presents a foundation model for medical applications that integrates multimodal data (e.g., imaging, text, signals) and supports multitask learning. The model is trained on diverse clinical datasets to perform diagnostics, prognostics, and treatment recommendations. It leverages deep learning architectures to unify heterogeneous data sources into a single predictive framework. The system is designed for deployment in clinical decision support tools.
Challenges / Opportunities: Medical AI models often lack generalizability across tasks and data types. This invention addresses the need for unified models that can handle complex clinical workflows. It opens opportunities for scalable AI deployment in hospitals and telemedicine platforms. The approach also supports continual learning and model adaptation.
Key Benefits / Advantages: ✔ Integrates multimodal clinical data ✔ Supports multitask learning ✔ Enhances diagnostic and treatment accuracy ✔ Scalable for clinical deployment
Applications: • AI-powered clinical decision support systems
Keywords: Medical AI, foundation model, multimodal data, multitask learning, clinical decision support
Intellectual Property: WO2024211177A1 (Application PCT/US2024/022211), Published March 29, 2024