Akademisyenlerin Beslenme ile İlgili Yapay Zeka Kullanımına Yönelik Bilgi Düzeyi ve Tutumlarının Değerlendirilmesi

Year 2025, Volume: 6 Issue: 2, 93 - 101, 30.09.2025

Ayden Özekinci , İrem Olcay Eminsoy

Abstract

Amaç: Bu çalışma, akademisyenlerin beslenme alanında yapay zeka (YZ) kullanımına yönelik bilgi ve tutumlarını incelemek amacıyla planlanmıştır.
Gereç ve Yöntem: Çalışma, Ekim 2024 ile Aralık 2024 tarihleri arasında gönüllü olarak çalışmaya katılmayı kabul eden 248 akademisyen üzerinde yürütülmüştür. Çalışmaya katılan akademisyenlere çevrimiçi anket formu ve Likert puanlama çizelgeleri uygulanmıştır. Verilerin değerlendirilmesinde SPSS 25.0 paket programı kullanılmıştır.
Bulgular: Çalışmaya 136 (%55) kadın ve 112 (%45) erkek olmak üzere toplam 248 kişi katılmıştır ve yaş ortalamaları 38,74±9,49 yıldır. Çalışmaya katılan akademisyenlerin %73,4'ü günlük yaşamlarında yapay zeka uygulamalarını kullandıklarını belirtmiştir. Erkek akademisyenlerin %52,6'sı ve kadın akademisyenlerin %53,8'i beslenme durumunu değerlendirmek için yapay zeka uygulamalarını kullandıklarını bildirmiştir. Erkek akademisyenlerin ortalama bilgi düzeyi puanı 13,71±7,60, ortalama tutum düzeyi puanı 40,56±10,35 iken, kadın akademisyenlerin ortalama bilgi düzeyi puanı 13,10±6,59, ortalama tutum düzeyi puanı 44,60±9,33 olarak bulunmuştur. Cinsiyete göre tutum düzeyi puanları arasında istatistiksel olarak anlamlı bir fark bulunmuştur (p<0.05).
Sonuç: Beslenme alanında yapay zeka uygulamaları her geçen gün artmaktadır. Bu uygulamalara yönelik bilgi ve tutum düzeyi kadın ve erkekler arasında farklılık göstermektedir. Tüm sağlık çalışanları, özellikle diyetisyenler, güncel yapay zeka algoritma uygulamaları ve bu uygulamaların doğruluğu konusunda eğitim almalı ve toplumun konu hakkındaki bilgi ve tutum düzeyi daha kapsamlı olarak incelenmelidir.

Keywords

Yapay zeka , beslenme değerlendirmesi , sağlık , bilgi , tutum

Ethical Statement

: Bu çalışma Başkent Üniversitesi Girişimsel Olmayan Klinik Araştırmalar Etik Kurulu Etik Kurulu tarafından onaylanmıştır (Onay tarihi 28.09.2024; Sayı: KA24/270).

Supporting Institution

Yazarlar maddi destek almadıklarını beyan ederler.

Thanks

Yazarlar, katılımcılara bu çalışmaya katılmaya gönüllü oldukları için teşekkür ederler.

Assessment of Academicians’ Knowledge Level and Attitudes Towards the Use of Artificial Intelligence Related to

Abstract

Aim: This study was planned to examine the knowledge and attitudes of academicians towards the use of artificial intelligence(AI) in the field of nutrition.
Materials and Methods: The study was conducted on 248 academicians who agreed to participate in the study voluntarily between October 2024 and December 2024. An online survey form and Likert scoring charts were applied to the academicians who participated in the study. SPSS 25.0 package program was used to evaluate the data.
Results: A total of 248 people, 136 (55%) female and 112 (45%) male, with an average age of 38.74±9.49 years, participated in the study. 73.4% of the academicians who participated in the study stated that they use artificial intelligence applications in their daily lives. 52.6% of male academicians and 53.8% of female academicians reported that they use artificial intelligence applications to assess nutritional status. While the average knowledge level score of male academics was 13.71±7.60 and the average attitude level score was 40.56±10.35, the average knowledge level score of female academics was 13.10±6.59 and the average attitude level score was 44.60±9.33. A statistically significant difference was found between the attitude level scores according to gender (p<0.05).
Conclusion: Artificial intelligence applications in the field of nutrition are increasing day by day. The level of knowledge and attitude towards these applications differs between women and men. All health workers, especially dietitians, should receive training on current artificial intelligence algorithm applications and the accuracy of these applications, and the level of knowledge and attitude of the society on the subject should be examined more comprehensively.

Keywords

Artificial intelligence , nutrition assessment , health , knowledge , attitude

Ethical Statement

This study was approved by Ethics Committee of Baskent University Non-Interventional Clinical Research Ethics Committee (Approval date 28.09.2024; Number: KA24/270)

Supporting Institution

The authors declare that they have no conflict of interest.

Thanks

The authors are grateful to the participants for their willingness to participate in this study.

References

• 1. McCarthy J, Minsky M, Rochester N, Shannon CEA. Proposal for the dartmouth summer research project on artificial intelligence, August 31, 1955. AI Mag. 2006;27(4):12-12. Available at: http://raysolomonoff.com/dartmouth/boxa/dart564props.pdf. Accessed on May 6, 2024.
• 2. Joshi S, Bisht B, Kumar V, Singh N, Jameel Pasha SB, Singh N, et al. Artificial intelligence assisted food science and nutrition perspective for smart nutrition research and healthcare. SMAB. 2024; 4(1):86-101.
• 3. Miyazawa T, Hiratsuka Y, Toda M, Hatakeyama N, Ozawa H, Abe C, et al. Artificial intelligence in food science and nutrition: A narrative review. Nutr Rev. 2022; 80(12): 2288-2300.
• 4. Sak J, Suchodolska M. Artificial intelligence in nutrients science research: a review. J Nut. 2021; 13(2):322.
• 5. Bronzwaer S, Kass G, Robinson TJ, Tarazona J, Verhagen H, Verloo D, et al. Food safety regulatory research needs 2030. EFSA J. 2019; 17(7): e170622.
• 6. Ülker İ, Çamli A. Beslenme ve diyetetik uygulamalarında yapay zeka. Bes Diy Derg. 2023; 51(2): 76-84. 7. Haynes SN, Richard DCS, Kubany ES. Content validity in psychological assessment: A functional approach to concepts and methods. Psychol Assess. 1995; 7(3), 238–247.
• 8. Davis, Fred D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly. 1989;13(3): 319-340.
• 9. Joshi A, Kale S, Chandel S, Pal DK. Likert Scale: Explored and explained. Br J Appl Sci Technol. 2015; 7(4), 396–403.
• 10. Serbaya SH, Khan AA, Surbaya SH, Alzahrani SM. Knowledge, attitude and practice toward artificial intelligence among healthcare workers in private polyclinics in jeddah, Saudi Arabia. Advances in Medical Education and Practice. 2024;15: 269-280.
• 11. Boateng, GO, Neilands TB, Frongillo EA, Melgar-Quinonez HR. Best practices for developing and validating scales for health, social, and behavioral research: a primer. Frontiers in public health. 2018; 6:149.
• 12. World Health Organization. Obesity: Preventing and managing the global epidemic. (WHO Technical Report Series, No. 894). 2020. https://apps.who.int/iris/handle/10665/42330. Accessed September 18, 2024.
• 13. Hamadeh S. Are artificial intelligence and co-active life coaching the future designers of nutrition and fitness matters. J Auton Intell. 2023; 6(2): 1-11.
• 14. Moreira LS, Chagas BC, Pacheco CSV, Santos HM, de Menezes LHS, Nascimento MM, et al. Development of procedure for sample preparation of cashew nuts using mixture design and evaluation of nutrient profiles by Kohonen neural network. Food Chem. 2019; 273:136–143.
• 15. Shima H, Masuda S, Date Y, Shino A, Tsuboi Y, Kajikawa M, et al. Exploring the impact of food on the gut ecosystem based on the combination of machine learning and network visualization. J Nutr. 2017; 9:1307.
• 16. Guo, J. Smartphone-powered electrochemical dongle for point-of-care monitoring of blood β-ketone. Anal Chem. 2017; 89(17):8609−8613.
• 17. Doherty A, Wall A, Khaldi N, Kussman M. Artificial intelligence in functional food ingredient discovery and characterisation: a focus on bioactive plant and food peptides. Front Genet. 2021; 12:768979.
• 18. Alshurafa N, Kalantarian H, Pourhomayoun M, Liu JJ, Sarin S, Shahbazi B, et al. Recognition of nutrition intake using time-frequency decomposition in a wearable necklace using a piezoelectric sensor. IEEE Sens. 2015; 15(7): 3909-3916.
• 19. Amft O, Troster G. On-body sensing solutions for automatic dietary monitoring. IEEE Pervasive Comput. 2009; 8(2):62-70.
• 20. Sempionatto JR, Montiel VRV, Vargas E, Teymourian H, Wang J. Wearable and mobile sensors for personalized nutrition. ACS sensors. 2021; 6(5):1745-1760.
• 21. Vu T, Lin F, Alshurafa N, Xu W. Wearable food intake monitoring technologies: A comprehensive review. J Comput. 2017; 6(1):4.
• 22. Eldridge AL, Piernas C, Illner AK, Gibney MJ, Gurinović MA, de Vries JHM, et al. Evaluation of new technology-based tools for dietary intake assessment-an ILSI Europe dietary ıntake and exposure task force evaluation. J Nutr. 2018; 11(1).
• 23. Fritsch SJ, Blankenheim A, Wahl A, Hetfeld P, Maassen O, Deffge S, et al. Attitudes and perception of artificial intelligence in healthcare: A cross-sectional survey among patients. Digit health. 2022; 8:1-16. 24. Castagno S, Khalifa M. Perceptions of artificial intelligence among healthcare staff: a qualitative survey study. Front Artif Intell. 2020; 3: 578983.
• 25. Manuel B. Garcia Chatgpt as a virtual dietitian: exploring its potential as a tool for improving nutrition knowledge. Appl Syst Innov. 2023; 6:96.
• 26. Vellido A. Societal issues concerning the application of artificial intelligence in medicine. Kidney Dis. 2019; 5(1):11-7.
• 27. Yu P, Song H, Gao J, Li B, Liu Y, Wang Y. Vitamin D (1,25-(OH)2D3) regulates the gene expression through competing endogenous RNAs networks in high glucose-treated endothelial progenitor cells. J. Steroid Biochem. Mol. Biol. 2019; 193: 105425.