Delirium prevention in the ICU is often achieved with non-pharmacological approaches, but some methods are difficult to implement due to their dependence on sporadic human observations. ADAPT, the Autonomous Delirium Monitoring and Adaptive Prevention system, will autonomously quantify two indicators of delirium (patient mobility and circadian desynchrony) and predict delirium trajectories, as well as prompt actions that decrease delirium risk factors (e.g., ambient light, nightly disruptions). Successful application of ADAPT would augment clinical decision-making and promote more targeted interventions.

While AI in critical care (AICC) has helped improve patient care, AICC research lacks a large generalized dataset, standards that support the exchange and use of health information (interoperability) between sites and researchers, and support from a workforce trained in AI and general public familiar with medical AI. CHoRUS, a multimillion dollar collaboration of 18 top medical research institutions, aims to solve these problems by creating a 100,000 patient dataset from a sample population with varied backgrounds, ethical standards that enable interoperability, and AI education for AICC researchers and the general public.

Although close monitoring and dynamic assessment of patient acuity are key aspects of ICU care, both are limited by time constraints imposed on healthcare providers. Dynamic and precise assessment of patient acuity relies almost exclusively on physicians’ clinical judgement and vigilance, and details indicating patient condition may be partially captured or completely missed by overburdened nurses. We aim to develop deep learning models for sensing, quantifying, and communicating any patient’s condition in an autonomous, precise, and interpretable manner. This tool could enhance clinical workflow and enable early intervention.

IDEALIST could advance the ability of healthcare providers to diagnose diseases by computing the risk of postoperative complications in real-time. Ultimately, the results are expected to improve patient outcomes, decrease hospitalization costs, and reduce lifelong complications. The proposed research is relevant to public health because it can result in enhanced perioperative care workflow and early intervention.

The Multi-Hospital Electronic Decision Support for Drug-Associated AKI (MEnD-AKI) project is aimed at assessing how effective a clinical surveillance system augmented with real-time predictive analytics can be in supporting pharmacist-led interventions to address drug-associated AKI (D-AKI).

Al Passport for Biomedical Research addresses the unique needs of a biomedical, behavioral, and clinical research workforce that has no previous training in Al. In addition to providing a foundational understanding of AI concepts and teaching AI skills, we train participants in its ethical and responsible use. Trustworthy AI is interdisciplinary. Our unique digital community transcends traditional boundaries, ensuring that Al Passport for Health’s educational experience prepares participants for a future where innovation meets impact. We foster a collaborative, open, and communicative environment, ensuring that participant insights contribute directly to the learning experience.

In January 2024, the University of Florida, Deloitte and NVIDIA joined forces to build JaxTwin, an advanced urban digital twin (UDT). This innovative tool will equip leaders with the ability to plan adaptation measures that will ensure the future health, resilience and economic vitality of Jacksonville’s residents.

Intelligent Hospital Digital Twin is an AI-powered, intelligent virtual ICU that represents the next step toward a health metaverse. By integrating real-time clinical data, predictive analytics, and machine learning, the technology simulates hospital environments, optimizes decision-making, and improves outcomes in critical care.

The Exposome Database is a comprehensive repository that compiles a wide range of data related to the social, economic, and environmental factors affecting health outcomes of populations. This database is crucial for public health research, policy-making, and interventions aimed at improving health and overall well-being.