Pacific Center for Artificial Intelligence and Data Science in Medicine (PAC-AID)

About This Grant

PROJECT SUMMARY – Overall It is predicted that virtually every type of clinician will use data science, such as artificial intelligence (AI) and machine learning (ML), in the future. Further, AI/ML is expected to play a dominant role across medical disciplines throughout the human lifespan. The utilization and ultimate training of deep learning models require specialized education and skills. The curation and processing of large datasets from locally sourced patient populations requires a curation and analysis team with specialized knowledge of transferring medical data between sites and potentially federated learning. Adding to this complexity is the potential for bias when diagnostic models are trained on datasets that don’t contain the characteristics of the population utilizing the model. There is a need to develop a talented AI and data science biomedical workforce that is trained in innovative approaches to working with large research datasets. In response to the Notice of Special Interest (NOSI): Supporting Data Sciences Research in IDeA States through the Centers of Biomedical Research Excellence (COBRE) Phase 1 Program (NOT-GM- 23-011), we propose to create the Pacific Center for Artificial Intelligence and Data Science in Medicine (PAC-AID). The long-term goal of the PAC-AID is to form self-sustaining core facilities and an AI and biomedical data science program that continually trains investigators to utilize advanced data science to utilize all available biomedical data sources to improve health outcomes in Hawaii and the Pacific region. The objective is to create a nucleating site for the use of AI/ML data science technologies to further our understanding of human health in communities in Hawaii. Specifically, we will 1) foster a next-generation workforce to conduct biomedical research using Artificial Intelligence and advanced machine learning; 2) develop interactive state-of-the-art research cores and projects that support training opportunities for local investigators and foster collaboration across the national IDeA network; 3) establish a supportive Advisory Committee and Mentoring Team of academic leaders and content experts to provide the investigators and RPLs guidance on the successful completion of the Center’s and RPLs’ goals. The expected outcome of the PAC-AID is to form a synergistic environment for investigators to integrate AI and advanced machine learning into problems that were not solvable before. The positive impact on the state and large US community will be experienced investigators in various fields who can take full advantage of complex clinical, translational, and basic research data using AI and advanced data science methods.