Artificial Intelligence in Risk Assessment and Prevention - 17/10/25

Doi : 10.1016/j.pop.2025.07.002

Rosemary Nabaweesi, DrPH, MPH, MBChB ^a,^⁎ , John Kyalo Muthuka, PhD ^b , Judith Leo, PhD ^c, Eugene Levin, PhD ^d
^a Health Policy, Department of Political Determinants of Health, School of Global Health, Meharry Medical College, 1005 Dr. D. B. Todd Jr. Boulevard, Nashville, TN 37208, USA
^b Department of Community Health and Health Promotion, Kenya Medical Training College, PO Box 30195-00100, Nairobi, Kenya
^c Department of Computational and Communication Science and Engineering, Nelson Mandela African Institution of Science and Technology, 404 Nganana, 2331 Kikwe, Arumeru P.O.BOX 447, Arusha, Tanzania
^d International Programs, Department of Biomedical Data Science, School of Applied Computational Sciences, Meharry Medical College, 1005 Dr. D.B. Todd Boulevard, Nashville, TN 37208, USA

^∗Corresponding author. Meharry Medical College, School of Global Health, Department of Political Determinants of Health, 1005 Dr. D. B. Todd Jr. Boulevard, Nashville, TN 37208.Meharry Medical CollegeSchool of Global HealthDepartment of Political Determinants of Health1005 Dr. D. B. Todd Jr. BoulevardNashvilleTN37208

Résumé

Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing primary care by enhancing diagnostic accuracy, risk prediction, and treatment planning. Integrating clinical data, wearable device data, and Social Determinants of Health with AI/ML allows primary care physicians to uncover hidden patterns, improve outcomes, and address environmental health challenges. The synergy of AI, interdisciplinary learning, and Geographic Information Systems enables personalized interventions and optimized decision making. Clinicians must integrate AI/ML carefully into workflows, overcoming challenges like data interoperability and ensuring transparency. Collaboration is key to creating effective, ethical AI tools that improve health care equity and efficiency.

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Keywords : Risk assessment, Artificial intelligence/Machine learning, Prediction, AI applications in primary care, Spatial data and AI