Artificial Intelligence in Nutrition & Dietetics (2010–2025): A Global Bibliometric and Evidence Mapping Study

Abstract

Aim: This study aimed to map global research activity on artificial intelligence (AI) in nutrition and dietetics between 2010 and 2025, identify major thematic areas, and determine methodological and translational gaps that influence clinical application.

Method: We used a descriptive bibliometric and evidence-mapping design, reported in line with PRISMA-ScR. Records published between 2010 and 2025 were retrieved from Web of Science (Core Collection), Scopus, and PubMed using AI- and nutrition-related keywords (full strategies in Supplement S1). After harmonization and two-stage deduplication (DOI → normalized title+year), the final corpus comprised 2.853 unique records spanning 2011–2024. We summarized descriptive indicators (annual production, journals, countries) and constructed keyword co-occurrence networks. Thematic clusters and methodological characteristics—data types, AI modalities, validation practices, and reporting signals—were evaluated against TRIPOD+AI, CONSORT-AI, SPIRIT-AI, and DECIDE-AI frameworks. No human participants or identifiable data were involved; ethics approval was not required.

Results: The number of publications increased markedly after 2019. Three dominant themes were identified: (i) clinical risk prediction and decision support using tabular and electronic health record data; (ii) image-based dietary assessment and nutrient-intake estimation; and (iii) personalization and counseling enabled by large language models (LLMs). Although performance metrics were frequently reported, external validation, calibration, fairness, and openness of code/data remained inconsistent across studies.

Conclusion: Research on AI in nutrition and dietetics has grown rapidly in the past decade, centering on predictive modeling, image-based assessment, and LLM-assisted personalization. However, methodological rigor and real-world validation are still limited. Future studies should prioritize standardized reporting, external validation, fairness assessment, and open science practices to ensure safe and generalizable implementation of AI in nutrition care.

Keywords

• Artificial intelligence
• nutrition
• dietetics
• bibliometric analysis
• evidence mapping

Beslenme ve Diyetetikte Yapay Zekâ (2010–2025): Bir Bibliyometrik ve Kanıt Haritalama Çalışması

Abstract

Amaç: Bu çalışma, 2010–2025 yılları arasında beslenme ve diyetetik alanında yapay zekâ (YZ) ile ilgili küresel araştırma faaliyetlerini haritalandırmak, temel tematik alanları belirlemek ve klinik uygulamayı etkileyen metodolojik ve dönüşümsel eksiklikleri ortaya koymak amacıyla yapılmıştır.

Yöntem: Çalışma, PRISMA-ScR ile uyumlu olarak raporlanan betimleyici bir bibliyometrik ve kanıt-eşleme tasarımına dayanmaktadır. 2010–2025 yılları arasında yayımlanan kayıtlar Web of Science (Core Collection), Scopus ve PubMed veri tabanlarından YZ ve beslenme anahtar sözcükleriyle taranmıştır (ayrıntılı stratejiler Ek S1’dedir). Uyumlaştırma ve iki aşamalı deduplikasyon (DOI → normalize başlık+yıl) sonrasında 2011–2024 aralığını kapsayan 2,853 benzersiz kayıt elde edilmiştir. Betimleyici göstergeler (yıllık üretim, dergiler, ülkeler) özetlenmiş; anahtar sözcük eş-ortaya çıkış ağları oluşturulmuştur. Tematik kümeler ve yöntemsel özellikler—veri türleri, YZ modaliteleri, doğrulama uygulamaları ve raporlama sinyalleri—TRIPOD+AI, CONSORT-AI, SPIRIT-AI ve DECIDE-AI çerçevelerine göre değerlendirilmiştir. İnsan katılımcı verisi kullanılmadığından etik kurul onayı gerekmemiştir.

Bulgular: Bulgulara göre 2019 yılından sonra yayın sayısında belirgin bir artış gözlenmiştir. Üç baskın tema tanımlanmıştır: (i) tablo ve elektronik sağlık kayıtlarına dayalı klinik risk tahmini ve karar desteği; (ii) görüntü tabanlı besin alımı ve diyet değerlendirmesi ve (iii) büyük dil modelleri (LLM) ile kişiselleştirilmiş beslenme danışmanlığı. Performans ölçütleri sık bildirilmekle birlikte, dış doğrulama, kalibrasyon, adalet (fairness) ve kod/veri açıklığı konularında tutarsızlıklar devam etmektedir.

Sonuç: Beslenme ve diyetetikte yapay zekâ araştırmaları son on yılda hızla artmış; öngörücü modelleme, görüntü tabanlı değerlendirme ve LLM destekli kişiselleştirme ekseninde yoğunlaşmıştır. Ancak metodolojik titizlik ve gerçek yaşam doğrulaması hâlâ sınırlıdır. Gelecekteki çalışmaların standart raporlama, dış doğrulama, adalet değerlendirmesi ve açık bilim uygulamalarına öncelik vermesi, YZ’nin beslenme alanında güvenli ve genellenebilir biçimde uygulanmasını sağlayacaktır.

Keywords

• Yapay zekâ
• beslenme
• diyetetik
• bibliyometrik analiz
• kanıt haritalama

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