Smart Prevention and Precision Care: Machine Learning in Cardiometabolic and Oncologic Diseases

Machine learning (ML) is driving a paradigm shift in the prevention and treatment of complex diseases, particularly in cardiometabolic and oncologic care. By harnessing the power of predictive analytics, precision risk assessment, and personalized treatment planning, ML is enabling earlier interventions, more accurate diagnoses, and targeted prevention strategies. With the rapid growth of multimodal biomedical data—from electronic health records and imaging to genomics, proteomics, and wearable sensors—ML offers unprecedented opportunities to extract clinically actionable insights that improve patient outcomes.

This Research Topic focuses on the transformative role of ML in advancing smart prevention and precision care for cardiometabolic and oncologic diseases. We aim to highlight innovative approaches that integrate predictive modeling, individualized treatment selection, and proactive health monitoring to address the unique challenges of cardiovascular, metabolic, and cancer-related conditions.

Key areas of interest include, but are not limited to:

• Predictive models for disease onset, progression, relapse, or treatment response in cardiometabolic or oncologic populations
• Personalized medicine strategies leveraging ML for patient stratification, therapy optimization, and biomarker discovery
• Preventive healthcare enabled by continuous monitoring, early warning systems, and adaptive risk models
• Integration of multimodal data (EHR, imaging, molecular profiling, wearable devices) for enhanced decision support
• Explainable and interpretable ML approaches that support clinician trust and adoption in cardiology, oncology, and metabolic medicine
• Ethical, regulatory, and implementation considerations specific to high-impact, high-risk clinical domains
• ML solutions for reducing disparities and improving access to advanced care in resource-limited settings

We welcome Original Research, Reviews, and Perspectives from interdisciplinary teams working at the interface of ML, cardiovascular medicine, oncology, and metabolic health. Submissions that include clinical validation, real-world deployment experiences, or share open-source tools and datasets are especially encouraged.

Together, let us explore how machine learning can enable smarter prevention and more precise care—transforming the future of cardiometabolic and oncologic medicine.

Keywords: Machine Learning, Personalized Care, Imaging, Genomics, Wearable Devices, Prevention

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.