THE UTILIZATION OF ARTIFICIAL INTELLIGENCE IN IMPROVING THE ACCURACY OF PERSONAL NUTRITION RECOMMENDATIONS FOR THE PREVENTION OF NON-COMMUNICABLE DISEASES: A SYSTEMATIC REVIEW
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This study aims to analyze the use of artificial intelligence in improving the accuracy of personal nutrition recommendations for the prevention of non-communicable diseases. The study used a systematic literature review method, following the PRISMA 2020 guidelines. Articles were searched in the Scopus, PubMed, Web of Science, and ScienceDirect databases for the 2021–2026 publication period. The keywords used include artificial intelligence, machine learning, deep learning, personalized nutrition, precision nutrition, dietary recommendations, and non-communicable diseases. Articles that meet the inclusion criteria are analyzed narratively and thematically based on the type of algorithm, personal data sources, accuracy indicators, health outcomes, and implementation limitations. The results of the study show that artificial intelligence has been used for food intake assessment, food type and portion recognition, metabolic response prediction, menu planning, dietary adherence monitoring, and preparation of personal nutrition recommendations. The integration of anthropometric, biomarker, physical activity, sleep patterns, microbiome, and health history allows for more adaptive recommendations than general nutritional guidelines. Some studies report improvements in dietary quality, weight, waist circumference, HbA1c, and triglycerides. However, clinical effectiveness has not been consistent due to data quality, algorithmic bias, limitations of local food databases, low external validation, and short duration of interventions. In addition, issues of transparency, privacy, data security, and access inequality remain the main challenges. It is concluded that artificial intelligence has the potential to support the prevention of non-communicable diseases, but its use should be implemented as a decision-support system involving nutritionists. Follow-up research requires multicenter clinical trials, standardization of accuracy indicators, validation across diverse populations, and strengthening of ethical regulations and data protection measures. This approach is expected to produce safe, equitable, contextual, evidence-based, and sustainable nutrition services optimally for the wider community
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