Table 3.

Examples of AI Support for Rehabilitation Nutrition Care Processes.

Rehabilitation nutrition assessment and diagnostic reasoning, rehabilitation nutrition diagnosis	Diagnosis of malnutrition, sarcopenia, and cachexia presence; diagnostic reasoning for causes of appetite loss, weight loss, and sarcopenia 　- Machine learning/deep learning
Rehabilitation nutrition goal setting	Weight goal setting based on integrated analysis of body composition (muscle mass, fat mass), activities of daily living, swallowing function, and other data 　- Machine learning, generative AI
Rehabilitation nutrition intervention	In addition to treatment recommended by clinical practice guidelines, proposes prioritized interventions such as optimal nutrition interventions and exercise programs based on causal reasoning 　- Reasoning AI, generative AI Support for individualized advice and motivation maintenance through AI chatbots to encourage behavioral change 　- Generative AI
Rehabilitation nutrition monitoring	Automatically estimates intake volume and nutrients from meal image analysis (pre- and postmeal) to adjust dietary plans 　- Deep learning

AI: artificial intelligence.