Artificial Intelligence and Machine Learning in Nutritional Healthcare: A Comprehensive Review of Recent Trends and Applications | ||
| Egyptian Journal of Health Sciences Technology | ||
| Articles in Press, Accepted Manuscript, Available Online from 29 October 2025 | ||
| Document Type: Original Article | ||
| DOI: 10.21608/ejhst.2025.406413.1008 | ||
| Authors | ||
| Alyaa Elrashedy* 1; saeed Awad2; Rewan Ahmed Abd El fatah2; Malak Abd El Nasser2; Talaat khafaga2; Mokhtar Rafat2; Ibrahim Helmy2; Mansour Osama2; Mostafa Sameer Mohammed2; Mohamed E. Hasan3 | ||
| 1Department of Animal Medicine and Infectious Diseases, Faculty of Veterinary Medicine, University of Sadat City, Egypt. Faculty of Applied Health Science, Borg Al Arab Technological University (BATU), Alexandria, Egypt | ||
| 2Faculty of Applied Health Science, Borg Al Arab Technological University (BATU), Alexandria, Egypt | ||
| 3Faculty of Applied Health Science, Borg Al Arab Technological University (BATU), Alexandria, Egypt Bioinformatics Department, Genetic Engineering and Biotechnology Research Institute, University of Sadat City, Egypt. | ||
| Abstract | ||
| This comprehensive literature review investigates the transformative role of artificial intelligence (AI) and machine learning (ML) in nutritional healthcare, focusing on their applications in personalized nutrition, dietary assessment, chronic disease management, and preventive healthcare. Through a systematic analysis of recent studies, we identify emerging trends, methodological approaches, implementation challenges, and future directions in this dynamic field. The findings highlight that AI and ML algorithms significantly enhance the precision and efficacy of nutritional interventions by enabling tailored dietary recommendations, improving dietary assessment accuracy, and optimizing chronic disease management strategies. These technologies leverage vast datasets to predict individual nutritional needs and health outcomes, fostering proactive healthcare approaches. However, challenges such as data quality, algorithmic bias, privacy concerns, and seamless clinical integration remain significant hurdles. The review underscores the necessity for interdisciplinary collaboration among nutritionists, healthcare providers, and data scientists to address these challenges and fully harness AI/ML’s potential, advancing evidence-based practices in nutritional healthcare for improved patient outcomes. | ||
| Keywords | ||
| Artificial intelligence; Digital health; Machine learning; Nutritional healthcare; Personalized nutrition | ||
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