Why AI?
UF AI Vision and Leadership

The mission of the university’s academic health center — UF Health — is to promote health through outstanding and high-quality patient care, innovative and rigorous education in the health professions and biomedical sciences, and high-impact research across the spectrum of basic, translational and clinical investigation. Our patients and our communities are at the center of all we do.

Graphic for UF Health AI research hub themes and conceptual organization
UF Health AI research hub themes

As part of the University of Florida’s Artificial Intelligence initiative, UF Health is creating an academic hub to advance AI in the health sciences grounded in the values of community, trustworthiness, and diversity, equity and inclusion. Together, these values foster team science and a translational vision of moving from big data and black boxes to explainable tools for improved health for all.

Led by the UF Health AI Steering Committee, the UF health colleges of Dentistry, Medicine, Nursing, Pharmacy, Public Health and Health Professions, and Veterinary Medicine are hiring at least 30 faculty positions to join UF’s growing community of researchers and clinicians developing and applying AI methods and tools in health care delivery, biomedical discovery, and public and population health.

Advancing research at UF Health

UF Health’s problem-solving culture generates real-world questions and data that spur advances in AI to improve health and health equity at individual, community, health system and population levels. UF Health research teams deploy AI techniques to harness big data and develop decision-support systems and predictive analytics that help patients, clinicians, health systems and payers optimize health care decisions. To optimize diagnostic and therapeutic development, our researchers are leveraging AI in tandem with UF Health’s data resources, imaging facilities, screening capabilities and sequencing technologies. UF Health faculty also are pioneering methods to link clinical, omics, social determinants, geospatial, environmental and other data for use with AI. Such linkages can help researchers identify pathways for health and disease and develop corresponding interventions to improve health. In embracing trustworthiness as a core value, we will prioritize rigorous methodology and data quality to ensure validity and strengthen the reproducibility, interpretability, generalizability and actionability of health-related AI applications.