Strengths, Limitations, and Future Role of AI–Based Diet Planning

Güzel Z. V., Ergan K.

4TH INTERNATIONAL ISTANBUL CONGRESS OF HEALTH SCIENCES , İstanbul, Türkiye, 12 - 13 Aralık 2025, ss.16, (Özet Bildiri)

  • Yayın Türü: Bildiri / Özet Bildiri
  • Basıldığı Şehir: İstanbul
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.16
  • İstanbul Yeni Yüzyıl Üniversitesi Adresli: Evet

Özet

Purpose: Artificial intelligence (AI) refers to computer programs that perform human-like thinking, reasoning, problem-solving, perception, and decision-making. AI use has increased recently and is widely applied for recommendations and task executon. Individuals ofen use these applicatons for health and nutrition advice. This study examines the reliability and limitations of AI-based diet planning and nutrition recommendations, as well as their impact on dietary adherence, nutrition awareness, and potential future applications. Methods: Articles published in English and Turkish between 2020 and 2025 were reviewed using databases such as Science Direct, Web of Science, Scopus, ULAKBİLİM, PubMed, and Cochrane Library. Keywords included “artificial intelligence and nutrition,” “personalized nutrition,” “nutrition planning,” and “AI software in nutrition.” Results: AI models provide detailed nutrient information and support nutrition awareness. However, they may occasionally eror in portion control and calculation of energy and nutrient content, leading to incorrect recommendations. In individuals with chronic diseases, pregnant, or breast feeding women, inaccurate suggestions may cause health issues. Additionally, AI cannot fully evaluate emotional states or personal preferences, limiting fully personalized plans. Conclusion: Studies indicate AI posiVvely affects nutrition, but its use alone is insufficient. It is recommended as a supportive tool under dietian guidance. A multidisciplinary approach involving nutrition and engineering can enhance AI’s accuracy and personalization. In the future, AI could track daily emotional states and meals, detect nutrient deficiencies—such as suggesting a bowl of yogurt for protein shortage—and provide evidence-based recommendations. Keywords: Artificial Intelligence, Diet Planning, Personalized Nutrition