Exploring AI-Driven Interventions for Preventing and Managing Malnutrition in Older Adults: A Systematic Review.

Abstract

Purpose: The primary objective of this systematic review is to explore the role of AI-driven interventions in preventing and managing malnutrition in older adults.

Methods: Using PRISMA guidelines, the authors searched electronic databases for relevant studies from the past five years. The authors appraised selected studies on AI-driven nutritional interventions for older adults using Critical Appraisal Skills Programme tools, assessing for potential bias before synthesizing the findings.

Results: The review found that AI-driven intervention, such as personalized meal plans and self-monitoring apps, hold promise for improving dietary habits. AI models also showed high accuracy in predicting malnutrition risks and screening for early signs, like swallowing difficulties. Preventative tools were identified as valuable for proactive care, and the importance of user-centred design and the role of healthcare professionals in improving technology adoption was highlighted.

Key conclusions: AI-driven interventions have significant potential for improving nutrition care for older adults through personalized strategies. However, challenges like usability, data privacy, and the need for validation in diverse settings must be addressed. Future research should focus on refining these technologies and including underrepresented populations to improve the health and quality of life of older adults.