Artificial Intelligence for Food Intake Measurement in Free-Living Settings
A special issue of Nutrients (ISSN 2072-6643). This special issue belongs to the section "Nutrition and Public Health".
Deadline for manuscript submissions: 5 September 2026 | Viewed by 189
Special Issue Editors
Interests: dietary behavior; public health nutrition; physical activity; obesity; diabetes; cognitive health; food environment; built environment; cost–benefit analysis; cost-effectiveness analysis; microsimulation; machine learning; policy analysis
Special Issues, Collections and Topics in MDPI journals
Interests: social policy; food insecurity; policy and program design; barriers to access; food as medicine; child nutrition programs; federal nutrition assistance programs
Special Issue Information
Dear Colleagues,
Accurate, scalable measurement of dietary intake in real-world, free-living settings remains a core bottleneck in public health nutrition and chronic-disease prevention research. Traditional methods (e.g., recalls, food records) are burdensome and error-prone, while gold-standard reference methods (e.g., weighed records, doubly labeled water) are costly and difficult to deploy at scale. Rapid advances in artificial intelligence are creating new opportunities to improve both the precision and feasibility of dietary assessment outside laboratory environments. This Special Issue will showcase innovative AI-enabled approaches for food intake measurement, including—but not limited to—computer vision for food recognition and portion estimation; multimodal assessment that integrates images with text/voice input and wearable or sensor data; large language models and machine learning for automated food logging, coding and nutrient estimation and integration with ecological momentary assessment and other real-time measurement frameworks. We welcome contributions addressing validation, calibration against reference methods, robustness across contexts and bias/fairness considerations to ensure equitable performance across diverse populations. Together, these advances can strengthen nutritional epidemiology, intervention research and precision public health.
Dr. Ruopeng An
Guest Editor
Dr. Dan Ferris
Co-Guest Editor
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Keywords
- computer vision
- large language model
- multimodal model
- machine learning
- artificial intelligence
- food intake measure
- dietary assessment
- bias
- fairness
- free-living setting
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