Personalized Preventive Medicine

The Personalized Preventive Medicine Section aims to establish research, innovation, and clinical practice that utilize individual-specific data to predict, prevent, and pre-empt the development of disease before it occurs. This Section serves as an interdisciplinary forum for submissions that integrate genomics, digital health, biomarkers, lifestyle information, AI-based risk stratification, and precision public health interventions, enabling anticipatory, patient-centered care.

Key areas of coverage include:

• Risk prediction programs and early detection platforms predicated on genetic, epigenetic, microbiome, metabolomic, or multi-omics information.
• Individualized preventive treatments, including behavioral, pharmacologic, nutritional, and lifestyle treatments.
• Digital and wearable health technologies for real-time tracking and individualized feedback.
• Artificial intelligence and machine learning applications for disease prevention and health risk stratification.
• Implementation science and health policies concerning personalized prevention approaches in clinical or community environments.
• Health equity and ethics in the application of personalized prevention resources.

This Section explores the intersection between personalized outcomes and scalable prevention practices, bridging the research–practice gap.