JMIR Publications

ABSTRACT

Background:

Obesity is a leading cause of preventable death worldwide. Artificial intelligence (AI), characterized by machine learning (ML) and deep learning (DL), has become an indispensable tool in obesity research.

Objective:

This review aims to provide researchers and practitioners with an overview of the AI applications to obesity research, familiarize them with popular ML and DL models, and facilitate the adoption of AI applications.

Methods:

We conducted a methodological review on the applications of AI to measure, predict, and treat obesity. We summarized and categorized AI methodologies used in the hope of identifying synergies, patterns, and trends to inform future investigations. We also provided a high-level, beginner-friendly introduction to the core methodologies to facilitate the dissemination and adoption of various AI techniques.

Results:

We identified 46 studies that employed diverse ML and DL models to assess obesity-related outcomes. Studies found AI models helpful in detecting clinically meaningful patterns of obesity or relationships between specific covariates and weight outcomes. The majority of studies comparing AI models with conventional statistical approaches found the former to achieve higher prediction accuracy on test data. Some studies comparing the performances of different AI models revealed mixed results, indicating the high contingency of model performance on the dataset and task it applied to. An accelerating trend of adopting state-of-the-art DL models over standard ML was observed to address challenging computer vision and natural language processing tasks. We concisely introduced the popular ML and DL models and summarized their specific applications in the studies included in the review.

Conclusions:

This study reviewed AI-related methodologies adopted in the obesity literature, particularly ML and DL models applied to tabular, image, and text data. The review also discussed emerging trends, such as multimodal/multi-task AI models, synthetic data generation, and human-in-the-loop, which may witness increasing applications in obesity research.