Integrating AI-Driven Predictive Analytics in Medical Imaging to Identify Digital Biomarkers of Chronic Diseases: Toward Proactive and Personalized Preventive Healthcare

Chronic diseases, including cardiovascular disorders, neurodegenerative conditions, and metabolic syndromes, represent the primary driver of global morbidity and healthcare expenditure. Traditionally, medical imaging has been utilized as a reactive diagnostic tool; however, the integration of Artificial Intelligence (AI) and predictive analytics is shifting this paradigm toward proactive intervention. This study explores the role of AI in identifying Digital Biomarkers, quantifiable, subtle physiological indicators extracted from radiological data that often precede clinical symptoms. By utilizing deep learning architectures and radiomics, AI models can detect sub-perceptible patterns in imaging, such as micro-structural changes in organ tissue, vascular calcification, or localized atrophy, to predict long-term health risks with high precision. These AI-driven insights allow for the transition from a one-size-fits-all diagnostic model to a personalized preventive framework, enabling clinicians to tailor interventions based on an individual’s unique digital profile. The findings suggest that integrating these predictive analytics into routine medical imaging not only enhances early-stage detection but also significantly improves the sustainability of healthcare systems by reducing the transition from chronic risk to acute disease. Ultimately, AI-driven digital biomarkers serve as a cornerstone for the future of personalized, proactive, and preventive medicine.

کلیدواژه ها:

Artificial Intelligence ، Digital Biomarkers ، Predictive Analytics ، Chronic Diseases ، Personalized Medicine ، Preventive Healthcare