The field of health monitoring is moving towards the integration of multimodal data, including images, text, and physiological signals, to improve diagnosis and treatment outcomes. Recent studies have demonstrated the effectiveness of deep learning models in combining different data modalities to enhance the accuracy of disease diagnosis and nutrition estimation. For example, the use of visual and ingredient features has improved nutrition estimation, while the fusion of glucose monitoring and food imagery has enhanced caloric content prediction. These multimodal approaches have the potential to revolutionize health monitoring and disease management.

Noteworthy papers include:

• A study on multi-modal wound classification using Xception and Gaussian Mixture Recurrent Neural Network, which achieved notable wound-class classifications with varying accuracy from 78.77 to 100%.
• A deep learning approach for early diagnosis of Metabolic Syndrome, which demonstrated the potential of using natural language processing and exercise monitoring to diagnose the condition with high positive results.
• A visual-ingredient feature fusion method for advancing food nutrition estimation, which introduced a new dataset and model-agnostic approach to improve nutrition estimation.
• A multimodal fusion framework for caloric content prediction, which jointly leveraged glucose monitoring and food imagery data to enhance caloric estimation.
• A point-cloud framework for food 3D reconstruction and volume estimation, which achieved superior performance in food volume estimation across multiple datasets.