Aug 2026· Nutrients· Vol 18· 0 citations· 115 references
Medicine
TL;DR
Overall, AI offers substantial opportunities to improve the personalization, efficiency, and scalability of clinical nutrition practice, but its safe and effective implementation will require continued validation, careful oversight, and integration with clinical expertise.
Abstract
Artificial intelligence (AI) is increasingly being applied in healthcare, with growing relevance to clinical nutrition. This narrative review examines current and emerging uses of AI in nutrition care within the Nutrition Care Process framework, with attention to assessment, monitoring and evaluation, diagnosis, intervention, and clinical support tools. Current applications include AI-assisted dietary assessment using image recognition, wearable sensors, analysis of continuous glucose and other physiologic data for early risk detection, and support for malnutrition screening and diagnosis. AI is also being explored for identifying micronutrient deficiencies and complications of nutrient excess, as well as for screening and early intervention in eating disorders. In nutrition intervention, AI has potential to support personalized dietary planning, nutrition support in intensive care settings, behavioral interventions, and precision nutrition approaches such as digital twins. Additional applications include clinical decision support and documentation assistance. However, despite its usefulness, concerns about AI systems exist. Its performance depends on the quality of the data used to train it; it can introduce bias, and it can produce inaccurate or misleading outputs. In addition, overreliance on AI may also reduce clinician attentiveness and contribute to cognitive errors. For these reasons, AI should be regarded as a support tool rather than a replacement for human clinical care. Overall, AI offers substantial opportunities to improve the personalization, efficiency, and scalability of clinical nutrition practice, but its safe and effective implementation will require continued validation, careful oversight, and integration with clinical expertise.
Artificial Intelligence (AI) is an emerging and rapidly expanding technology in nutrition that holds significant potential to revolutionise the field. AI and its algorithms, is capable of understanding complex interactions, analysing complex data and interpreting images. The key applications of AI in nutrition field are in personalized nutrition and diet planning, dietary intake assessment and monitoring, prevention of malnutrition, disease prevention and management, virtual nutritional guidance, food product development and nutrition research. AI is reshaping the field of nutrition in ways that were unimaginable in the previous era. Through their accuracy and efficiency, wearable devices, and chatbot applications are revolutionizing the nutrition field. However, AI applications are not well known in this field, especially in low- and middle-income countries. AI applications may be adapted and applied in nutrition, but they need to be investigated. Addressing data privacy and bias, ensuring the accuracy of AI algorithms and overcoming ethical concerns are major future challenges. Need of the hour is interdisciplinary collaboration between AI experts and nutritionists for more seamless, effortless and accurate results.
Sangeeta Kulshrestha· Adolescência e Saúde· 0 citations
Primary prevention is essential to reduce disease burden before clinical onset, yet it remains less systematically integrated into care than diagnosis and treatment. Although artificial intelligence (AI) is increasingly used in medicine, most applications have focused on secondary and tertiary prevention, including diagnosis, prognostic stratification, and disease management, while its role in primary prevention remains less defined. This narrative review examines current AI applications across four modifiable lifestyle domains relevant to prevention and healthspan promotion: nutrition, physical activity, sleep, and mental health. We synthesize evidence on machine-learning models, wearable-derived algorithms, computer-vision tools, just-in-time adaptive interventions, and conversational agents used in consumer, community, and hybrid clinical–digital settings. AI applications support postprandial glycemic prediction, automated dietary assessment, meal-planning adherence, sedentary-pattern detection, personalized exercise recommendations, adaptive behavioral nudges, sleep monitoring, circadian-aware recommendations, psychoeducation, stress-management support, and early identification of psychological vulnerability. Collectively, these tools may extend prevention beyond episodic clinical encounters toward continuous, personalized, and context-aware support. However, evidence remains limited by short follow-up, reliance on surrogate or engagement outcomes, digitally literate populations, and insufficient validation in real-world preventive-care pathways. AI is therefore a promising enabling technology for proactive, healthspan-oriented medicine, provided future studies demonstrate long-term effectiveness, equity, safety, and responsible implementation.
Katia Iaccarino, F. Ongaro, Luca Di Palma et al.· Applied Sciences· 0 citations
Artificial Intelligence (AI) is revolutionizing health and nutrition, significantly advancing personalized dietary management and health optimization. Technologies like machine learning algorithms, data mining, and predictive models enable the creation of individualized diet plans based on genetic data, lifestyle habits, and biometric information, offering more effective and precise nutrition recommendations. AI-enabled systems provide personalized nutritional strategies for these diseases, offering real-time dietary suggestions tailored to individual needs. Additionally, AI optimizes nutritionists' and healthcare providers' recommendations by analyzing the effects of nutrition programs on health outcomes. In the food industry, AI promises advancements in food safety, quality control, product innovation, and sustainable food production. However, the widespread adoption of AI in nutrition faces challenges, including ethical concerns, data privacy issues, and algorithmic biases. Protecting personal health data and ensuring AI systems are fed with accurate, unbiased data are crucial in addressing these challenges. This review highlights AI's potential in nutrition science and health management, identifying opportunities for future research and innovation.
Hatice Karakoç, Eda Çaycı, Seda Çiftçi· Journal of NutriLife· 0 citations
Rapid advances in technology, particularly the integration of artificial intelligence (AI) technology into our lives, have increased interest in digital twin (DT) technology, which is a dynamic virtual model of a physical system. In the healthcare sector, DT is also seen as having the potential to bring about lasting transformation in areas such as drug development, advanced diagnostics and preventive treatment, clinical research, and personalized medicine. In the coming years, DT is expected to enable personalized nutrition by integrating genetic, epigenetic, microbiome, metabolic, immunological, and lifestyle data into comprehensive virtual models. This new paradigm has the potential to provide groundbreaking opportunities for the management and prevention of various nutrition-related diseases, obesity, and support healthy aging. Although early research in the field of nutrition is promising, various challenges and limitations persist, including data standardization, privacy, data quality and security, ethical concerns, high costs, scalability, and clinical validation issues. Currently, the use of DT in the field of nutrition and dietetics remains in the proof-of-concept stage. Nevertheless, it is anticipated that as these limitations are overcome over time, this technology will transform and guide global healthcare systems. This narrative review defines the concept of DT from a healthcare perspective, summarizes its current applications in nutrition and dietetics, and outlines its high potential, key limitations, and challenges.
Burcu Aksoy Canyolu, Nilüfer Şen· İstanbul Gelişim Üniversites...· 0 citations
Artificial intelligence (AI) is entering child nutrition through dietary assessment, malnutrition forecasting, clinical decision support, meal recommendation, conversational interventions, and food-environment monitoring. The consequences of these applications converge in everyday eating. This Mini Review synthesizes evidence on how AI measures nutritional states and mediates food choice, communication, sensory acceptance, family practice, and digital exposure. The evidence supports two connected functions. As nutritional intelligence, AI converts clinical, dietary, and environmental data into assessments or predictions. As gastronomic mediation, it participates in decisions about what foods are noticed, recommended, prepared, discussed, and accepted. Direct pediatric validation is strongest for bounded assessment and forecasting tasks. Child meal-planning studies and generative-AI evaluations based on standardized adolescent profiles reveal a gap between nutrient optimization, culinary coherence, and nutritional safety. A large adolescent chatbot trial combined scalable delivery with null intention-to-treat effects on diet quality and BMI trajectory. Co-design and behavioral studies further identify familiarity, texture, participation, and caregiver involvement as central design variables. We propose five iterative translational gates: technical validity, nutritional validity, behavioral acceptability, contextual legitimacy, and real-world effectiveness and implementation. Future research should combine age-specific nutritional constraints with sensory and cultural knowledge, evaluate performance across food cultures, preserve professional and caregiver oversight, and test outcomes in homes, schools, clinics, and digital food environments. AI can advance child-focused gastronomy by translating computational outputs into safe, culturally meaningful, and developmentally appropriate eating practices.
L. Xing, Xue Wu· Frontiers in Nutrition· 0 citations
Artificial intelligence should be viewed as a complement to multidisciplinary HEN expertise, and the strongest near-term opportunities are clinician-supervised patient education, symptom triage, adherence support, remote monitoring, and workflow automation.
Danielle P. Johnson, Edwin Feghali, Osman Mohamed Elfadil et al.· Nutrients· 0 citations
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