Beslenme ve Diyetetikte Yapay Zekâ Destekli Dijital İkiz Yaklaşımları: Kanıtlar, Potansiyel ve Sınırlılıklar

Öz

Teknolojideki hızlı ilerlemeler, özellikle yapay zeka teknolojisinin yaşamın pek çok alanına girmesiyle birlikte, fiziksel sistemlerin dinamik sanal modelleri olan dijital ikizler farklı sektörlerde kullanılmaya başlanmıştır. Hayati sektörlerden sağlık sektöründe de dijital ikiz teknolojisi; ilaç geliştirme, ileri tanı ve koruyucu tedavi, klinik araştırmalar ve kişiselleştirilmiş tıp alanlarında kalıcı dönüşüm vaat eden yüksek potansiyeller olarak görülmektedir. Gelecek yıllarda dijital ikizlerin, genetik, epigenetik, mikrobiyom, metabolik, bağışıklık ve yaşam tarzı verilerini bütüncül bir şekilde değerlendirerek kişiselleştirilmiş beslenmeyi mümkün kılması beklenmektedir. Bu yeni paradigma; obezite başta olmak üzere beslenmeyle ilişkili birçok hastalığın yönetimi, önlenmesi ve sağlıklı yaş almanın desteklenmesi açısından çığır açıcı araçlar sunma potansiyeline sahiptir. Mevcut durumda beslenme alanında yapılan çalışmalar umut vaad ederken; veri standardizasyonu, gizlilik, veri kalitesi ve güvenliği, etik kaygılar, yüksek maliyet, ölçeklenebilirlik ve klinik doğrulama gibi çeşitli zorluk ve sınırlamalar devam etmektedir. Bu nedenle beslenme ve diyetetik alanında dijital ikiz teknolojisinin kullanımı henüz kanıtlanma aşamasındadır. Ancak zaman içinde bu sınırlamaların aşılmasıyla bu teknolojinin sağlık hizmetlerini ve sistemini evrensel ölçekte dönüştürücü ve yönlendirici bir konuma geçeceği öngörülmektedir. Bu geleneksel derlemede sağlık alanı perspektifinden dijital ikiz kavramı tanımlanmış; beslenme ve diyetetik alanında dijital ikiz uygulamalarının mevcut durumu sunulmuş; ayrıca yüksek potansiyelinin yanında karşılaşılan sınırlamalar ve zorluklara yer verilmiştir.

Anahtar Kelimeler

• Dijital ikiz
• yapay zeka
• insan dijital ikizi
• kişiselleştirilmiş beslenme
• veri temelli beslenme

Destekleyen Kurum

Yazarlar bu çalışmanın finansal herhangi bir destek almadığını beyan etmişlerdir.

Etik Beyan

Yazarların beyan edecekleri herhangi bir çıkar çatışması bulunmamaktadır.

Teşekkür

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AI Enabled Digital Twin Approaches in Nutrition and Dietetics: Evidence, Potential, and Limitations

Öz

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.

Anahtar Kelimeler

• Digital twin
• artificial intelligence
• human digital twin
• personalized nutrition
• data-driven nutrition science

Destekleyen Kurum

The authors declared that this study has no financial support.

Etik Beyan

The authors have no conflicts of interest to declare.

Teşekkür

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Kaynakça

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