Multimodal AI for Aging: Integrating Clinical, Imaging, Sensor, and Omics Data

Aging is a complex, multi-system process that shapes health trajectories and risk for age-related outcomes across the life course. At the same time, aging research is generating rapidly expanding datasets—ranging from electronic health records and laboratory measures to medical imaging, wearable and mobile sensor streams, and high-dimensional omics profiles. These modalities capture complementary signals, but they are often analyzed in isolation, limiting our ability to build coherent, clinically useful models of aging.

This Research Topic focuses on computational, AI, and machine-learning approaches for modeling aging and age-related outcomes. We encourage submissions that leverage multimodal data to generate interpretable, actionable insights into aging, including approaches that improve prediction, phenotyping, risk stratification, and mechanistic understanding. Particular emphasis is placed on models that are transparent, robust, and designed for translation—supporting reproducibility, fairness, and deployment across diverse populations and real-world settings.

We welcome contributions that address (but are not limited to):

1. Multimodal integration of clinical, imaging, sensor/wearable, and omics data for aging-related research
2. AI/ML methods for biological age estimation, aging clocks, and digital biomarkers
3. Prediction of age-related outcomes (e.g., frailty, functional decline, multimorbidity, hospitalization, mortality)
4. Interpretable and causal modeling approaches linking multimodal features to aging mechanisms and trajectories
5. Validation, benchmarking, and external generalization across cohorts, sites, and populations
6. Bias, fairness, privacy, and governance considerations in AI for aging
7. Tools, pipelines, and best practices that enable reproducible multimodal aging research

This Topic aims to bring together method developers and applied researchers to accelerate the development of reliable multimodal AI systems that advance aging science and support decision-making in research and clinical practice.

Article types and fees

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

• Brief Research Report
• Clinical Trial
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• ... 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
• Clinical Trial
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Technology and Code

Keywords: Multimodal machine learning, Biological age, Wearable sensors / digital biomarkers, Multi-omics integration, Interpretable AI

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.