EMED: An Ethical Mixture-of-Experts Digital Twin Framework for Medical Device Surveillance

About This Grant

ABSTRACT Medical devices, a critical component of healthcare infrastructure, play an essential role in clinical interventions and represent a significant portion of healthcare expenditure. The FDA specifically focuses on complex medical devices, encompassing a range of complexities from advanced materials and intricate mechanisms to software integration and combined products like device-drug or device-biologic combinations. Despite a streamlined regulatory pathway for these devices, ensuring their safety and efficacy remains a formidable challenge. This project aims to enhance the monitoring of complex medical devices in the post-market phase by integrating a wide array of data sources, including Cosmos, IQVIA, MAUDE, claims data, local EHRs at UTHealth, alongside unstructured clinical notes, social media, and device recall data. The project will develop a suite of Mixture-of-Expert (MoE) tools and corresponding open-source software designed for processing rich, ethically sourced multimodal data to facilitate their use in downstream predictive tasks like adverse event detection. Our MoE frameworks will 1) repurpose state-of-the-art pre-trained models from diverse modalities and aggregate these unimodal models as a series of experts; 2) learn modality-aware routing to synergize the modeling of heterogeneous modalities; and 3) co-design with ethical regularizations to promote multimodal privacy and fairness. These tools, featuring user-friendly APIs with comprehensive explanations, will be made accessible to the biomedical research community, enabling researchers not currently specializing in Al/ML to leverage these advanced tools in their medical device surveillance efforts. This careful ethics-multimodal co-design is crucial for identifying device functionality and patient safety issues through improved post-market surveillance.