Artificial Intelligence for Multi-Omics and Medical Imaging in Major Non-Communicable Diseases

Non-communicable diseases (NCDs), including cardiovascular disease, cancer, diabetes, chronic respiratory conditions, and neurological disorders, represent the leading cause of global mortality, accounting for over 74% of all deaths worldwide. Despite advances in prevention and treatment, early and accurate diagnosis remains a critical challenge, and the biological complexity underlying these diseases continues to limit therapeutic progress.

The convergence of multi-omics technologies and medical imaging has opened new avenues for understanding NCDs at unprecedented resolution. Multi-omics approaches spanning genomics, transcriptomics, proteomics, metabolomics, and epigenomics capture the molecular architecture of disease, while medical imaging provides structural, functional, and morphological insights at the tissue and organ level. Integrating these complementary data modalities holds enormous promise for identifying novel biomarkers, stratifying patient risk, and enabling precision medicine.

Artificial intelligence (AI), and in particular machine learning and deep learning, is uniquely positioned to unlock the potential of these large, high-dimensional, and heterogeneous datasets. AI-driven methods can detect patterns across omics layers, extract quantitative features from imaging data, and build multimodal models that bridge molecular and clinical phenotypes, capabilities far beyond the reach of traditional analytical approaches.

This Research Topic invites contributions that advance the application of AI to multi-omics data, medical imaging, or their integration in the context of major NCDs. We welcome original research, reviews, and methodological articles covering, but not limited to:

Deep learning and machine learning models for omics data analysis

AI-based image analysis and radiomics in NCD diagnosis and prognosis

Multimodal AI frameworks integrating omics and imaging data

Biomarker discovery and patient stratification using AI

Federated and privacy-preserving learning approaches for clinical data

Explainable AI (XAI) methods in clinical and translational settings

AI-driven drug target identification and therapeutic response prediction

By bringing together expertise from computational biology, clinical medicine, medical imaging, and data science, this Research Topic aims to accelerate the translation of AI-powered multi-omics and imaging approaches into clinically meaningful tools that improve outcomes for patients with major non-communicable diseases.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy Brief
• Review
• Systematic Review
• Technology and Code

Keywords: artificial intelligence, multi-omics, medical imaging, deep learning, non-communicable diseases, biomarker discovery, precision medicine, multimodal integration, radiomics, machine learning

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.