AI and IoT in Nutritional Science: Transforming Digestion Research and Precision Nutrition. - PUSHPARAJ, POOJITHA; MOHAN, LAKSHMI; AMBIKALEKSHMI, ANJU KANICHERIL; CHERIAN, ELSA; RAJAN, ROSAMMA; KUMAR, NANDHA (Current Research in Nutrition & Food Science, 2026)
This review synthesizes current advancements and applications of these technologies in the context of human digestion and personalized nutrition. The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) in nutritional science has transformed dietary monitoring, digestion research, and precision nutrition. AI-driven models, including machine learning and deep learning, enable accurate predictions of nutrient metabolism, glycemic responses, and dietary impacts on health. IoT devices, such as ingestible sensors, wearable trackers, and smart kitchen appliances, facilitate real-time monitoring of food intake, metabolic responses, and digestive processes. These technologies enhance research accuracy, optimize food formulation, and support personalized dietary recommendations. Additionally, IoT-driven automation improves food production and safety, reducing waste and enhancing sustainability. Collectively, these tools have demonstrated the potential to improve dietary adherence, optimize metabolic outcomes, and inform public health strategies. However, challenges related to data security, interoperability, and ethical concerns must be addressed for broader implementation. As AI and IoT continue to evolve, their role in nutritional science will drive innovations in food technology, precision health, and public health initiatives, offering more effective and individualized dietary interventions. Future research should aim to integrate multi-sensor data streams and AI-driven analytics for real-time, adaptive nutrition interventions.