University of Utah Researchers Launch AI Toolkit for Early Disease Prediction
Salt Lake City, Utah, Wednesday, 30 April 2025.
The University of Utah introduces RiskPath, an open-source AI tool that predicts chronic diseases years before symptoms appear, improving preventive healthcare.
Breakthrough in Disease Prediction Accuracy
The newly developed RiskPath software demonstrates unprecedented accuracy in disease prediction, achieving success rates between 85% and 99%, a significant improvement over existing medical prediction systems that typically identify at-risk patients with only 50-75% accuracy [1]. This advancement comes at a crucial time, as chronic and progressive diseases currently account for more than 90% of healthcare costs and mortality [1].
Streamlined Implementation for Healthcare Providers
While capable of analyzing hundreds of health variables, researchers at the University of Utah’s Department of Psychiatry and Huntsman Mental Health Institute have optimized RiskPath to operate effectively using just 10 key health factors [1]. The system has been rigorously validated across three major patient cohorts, including testing with the ABCD study (n=10,093), successfully predicting eight different conditions including depression, anxiety, ADHD, hypertension, and metabolic syndrome [2].
Integration with Existing Healthcare Systems
The University of Utah Health system, which serves residents across five surrounding states through its network of 5 hospitals and 12 community clinics [3], provides an ideal testing ground for RiskPath’s implementation. The tool’s integration potential extends to various specialties, from primary care to cardiovascular services, enabling comprehensive preventive healthcare strategies across multiple medical disciplines [3].
Future Applications and Development
Looking ahead, the research team is actively exploring RiskPath’s integration into clinical decision support systems and preventive care programs [1]. The tool’s open-source nature facilitates continuous improvement and adaptation across different healthcare settings, while its explainable AI framework ensures transparency in medical decision-making processes [1]. The technology’s potential impact on early intervention strategies could transform how healthcare providers approach disease prevention and management [1].