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The Use of Artificial Intelligence in Nutrition Science

Year 2023, Volume: 3 Issue: 2, 9 - 13, 02.09.2023

Abstract

Artificial intelligence is a branch of science based on imitating the human brain. It aims to be able to do the activities in the same way that people can do thanks to their intelligence, and even go beyond it. The use of artificial intelligence in the field of nutrition is a very current field. For this reason, since it is a very new approach, there is a lack of data. The fact that nutrition is the basis of health and the widespread use of the concept of personalized nutrition, especially in recent years, increases the interest to artificial intelligence. Making use of artificial intelligence while making personalized diet plans rather than a general diet application and at the same time providing nutritional status assessment is a promising new window for the future. There is an approach that argues that artificial intelligence approaches may be safer than traditional methods, but it is necessary to support this theory with scientific data. It is necessary for nutritionists to support the studies to be carried out in order to realize the use of artificial intelligence in many different fields of nutrition science and to expand its use worldwide.

References

  • 1.Hüseyin Fırat Kayıran HG. Yapay Zekanın Gıda Mühendisliği Alanında Kullanılabilirliği. Mersin Akademi Yayınları. 2021.
  • 2.Öztürk K, Şahin ME. Yapay sinir ağları ve yapay zekâya genel bir bakış. Takvim-i Vekayi. 2018;6(2):25-36.
  • 3. Haenlein M, Kaplan A. A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California management review. 2019;61(4):5-14.
  • 4.Moor J. The Dartmouth College artificial intelligence conference: The next fifty years. Ai Magazine. 2006;27(4):87-.
  • 5.Sak J, Suchodolska M. Artificial intelligence in nutrients science research: a review. Nutrients. 2021;13 (2):322.
  • 6. Miyazawa T, Hiratsuka Y, Toda M, Hatakeyama N, Ozawa H, Abe C, et al. Artificial intelligence in food science and nutrition: a narrative review. Nutrition Reviews. 2022:80(12): 2288-300.
  • 7.Matusheski NV, Caffrey A, Christensen L, Mezgec S, Surendran S, Hjorth MF, et al. Diets, nutrients, genes and the microbiome: recent advances in personalised nutrition. British Journal of Nutrition. 2021;126(10):1489-97.
  • 8. Ülker İ, Ayyıldız F. Artificial Intelligence Applications in
  • Nutrition and Dietetics. JOURNAL OF INTELLIGENT
  • SYSTEMS WITH APPLICATIONS. 2021;4(2).
  • 9. Kelly J, Collins P, McCamley J, Ball L, Roberts S, Campbell K. Digital disruption of dietetics: are we ready? Journal of Human Nutrition and Dietetics. 2021:34(1):134-46 10.Choi RY, Coyner AS, Kalpathy-Cramer J, Chiang MF, Campbell JP. Introduction to machine learning, neural networks, and deep learning. Translational Vision Science & Technology. 2020;9(2):14-.
  • 11. Thomas DM, Kleinberg S, Brown AW, Crow M, Bastian ND, Reisweber N, et al. Machine learning modeling practices to support the principles of Al and ethics in nutrition research. Nutrition & Diabetes. 2022;12(1):48.
  • 12.Oh YJ, Zhang J, Fang M-L, Fukuoka Y. A systematic review of artificial intelligence chatbots for promoting physical activity, healthy diet, and weight loss. International Journal of Behavioral Nutrition and Physical Activity. 2021;18:1-25.
  • 13.Zhang J, Oh YJ, Lange P, Yu Z, Fukuoka Y. Artificial
  • intelligence chatbot behavior change model for designing artificial intelligence chatbots to promote physical activity and a healthy diet. Journal of medical Internet research. 2020;22(9):e22845.14.Iceta S, Tardieu S, Nazare J-A, Dougkas A, Robert M, Disse E. An artificial intelligence-derived tool proposal to ease disordered eating screening in people with obesity. Eating and Weight Disorders-Studies on Anorexia, Bulimia and Obesity. 2021:1-5.15. Allen B, Lane M, Steeves EA, Raynor H. Using Explainable Artificial Intelligence to Discover Interactions in an Ecological Model for Obesity. International Journal of Environmental Research and Public Health. 2022;19(15):9447.16.DeGregory K, Kuiper P, DeSilvio T, Pleuss J, Miller R, Roginski J, et al. A review of machine learningin obesity. Obesity reviews. 2018;19(5):668-85.
  • 17.Sempionatto JR, Montiel VR-V, Vargas E, Teymourian H. Wang J. Wearable and mobile sensors for personalized nutrition. ACS sensors. 2021;6(5): 1745-60.
  • 18.Muzny M. Henriksen A, Giordanengo A. Muzik J, Grøttland A, Blixgård H, et al. Wearable sensors with possibilities for data exchange: Analyzing status and needs of different actors in mobile health monitoring systems. International journal of medical informatics. 2020; 133: 104017.
  • 19.Howard R, Guo J. Hall KD. Imprecision nutrition? Different simultaneous continuous glucose monitors provide discordant meal rankings for incremental postprandial glucose in subjects without diabetes. The American Journal of Clinical Nutrition. 2020;112(4): 1114-9.
  • 20. Kumar P, Sinha R, Shukla P. Artificial intelligence and synthetic biology approaches for human gut microbiome. Critical Reviews in Food Science and Nutrition. 2022;62(8): 2103-21.
  • 21.Kenner B, Chari ST, Kelsen D, Klimstra DS, Pandol SJ,
  • Rosenthal M, et al. Artificial intelligence and early detection of pancreatic cancer: 2020 summative review. Pancreas. 2021;50(3):251. 22.Zeevi D, Korem T, Zmora N, Israeli D, Rothschild D,
  • Weinberger A, et al. Personalized nutrition by prediction of glycemic responses. Cell. 2015;163(5):1079-94. 22.Zeevi D, Korem T, Zmora N, Israeli D, Rothschild D,
  • Weinberger A, et al. Personalized nutrition by prediction of glycemic responses. Cell. 2015;163(5):1079-94. 23.Addanki M, Patra P, Kandra P. Recent advances and
  • applications of artificial intelligence and related technologies in the food industry. Applied Food Research. 2022:100126. 24.Sadhu T, Banerjee I, Lahiri SK, Chakrabarty J. Enhancement of nutritional value of fried fish using an artificial intelligence approach. Environmental Science and Pollution Research. 2021:1-16.
  • 25.Mortazavi BJ, Gutierrez-Osuna R. A review of digital innovations for diet monitoring and precision nutrition. Journal of diabetes science and technology. 2023;17(1):217-23.
  • 26.Zahid M, Sughra U. Development of a protocol for conducting a randomized control trial on effects of artificial intelligence on nutritional status of children post cardiac surgery.
  • JPMA. 2022:72(908).
  • 27.Pekcan AG. Dijital Sağlık: Beslenme ve Diyetetik Bilim
  • Dalında Yaklaşım. Beslenme ve Diyet Dergisi. 2022;50(1):1-6. 28. Petot GJ, Marling C, Sterling L.. An artificial intelligence system for computer-assisted menu planning. Journal of the American Dietetic Association. 1998;98(9): 1009-14.
  • 29.Seljak BK. Computer-based dietary menu planning.
  • Journal of food composition and analysis. 2009;22(5):414-20. 30.Gaál B, Vassányi I, Kozmann G. A novel artificial intelligence method for weekly dietary menu planning. Methods of Information in Medicine. 2005;44(05):655-64.
  • 31.Milne-Ives M. de Cock C. Lim E, Shehadeh MH, de Pennington N, Mole G, et al. The effectiveness of artificial intelligence conversational agents in health care: systematic review, Journal of medical Internet research. 2020;22(10): e20346.

Yapay Zekanın Beslenme Biliminde Kullanımı

Year 2023, Volume: 3 Issue: 2, 9 - 13, 02.09.2023

Abstract

Yapay zeka, insan beynini taklit etme temelli bir bilim dalıdır. İnsanın zekası sayesinde yapabildiği faaliyetleri aynı şekilde yapabilmeyi hatta bunun ötesine geçmeyi hedeflemektedir. Beslenme alanında yapay zekanın kullanımı ise oldukça güncel bir alandır. Bu sebeple çok yeni bir yaklaşım olduğu için de veri yetersizliği söz konusudur. Beslenmenin sağlığın temeli olması ve özellikle son yıllarda kişiye özel beslenme kavramının yaygınlaşması yapay zekaya yönelimi artırmaktadır. Genel bir diyet uygulamasından ziyade kişiye özel diyet planlamalarını yaparken yapay zekadan faydalanmak ve aynı zamanda beslenme durum değerlendirmesini sağlayabilmek gelecek için umut vadeden yeni bir penceredir. Yapay zeka yaklaşımlarının geleneksel yöntemlere kıyasla daha güvenli olabileceğini savunan bir yaklaşım vardır ancak bu teoriyi bilimsel verilerle desteklemek gereklidir. Beslenme uzmanlarının, yapay zekanın beslenme bilimin çok daha farklı alanlarında da kullanımını gerçekleştirebilmek ve dünya çapında kullanımını yaygınlaştırmak için yapılacak çalışmalara destek vermesi gereklidir.

References

  • 1.Hüseyin Fırat Kayıran HG. Yapay Zekanın Gıda Mühendisliği Alanında Kullanılabilirliği. Mersin Akademi Yayınları. 2021.
  • 2.Öztürk K, Şahin ME. Yapay sinir ağları ve yapay zekâya genel bir bakış. Takvim-i Vekayi. 2018;6(2):25-36.
  • 3. Haenlein M, Kaplan A. A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California management review. 2019;61(4):5-14.
  • 4.Moor J. The Dartmouth College artificial intelligence conference: The next fifty years. Ai Magazine. 2006;27(4):87-.
  • 5.Sak J, Suchodolska M. Artificial intelligence in nutrients science research: a review. Nutrients. 2021;13 (2):322.
  • 6. Miyazawa T, Hiratsuka Y, Toda M, Hatakeyama N, Ozawa H, Abe C, et al. Artificial intelligence in food science and nutrition: a narrative review. Nutrition Reviews. 2022:80(12): 2288-300.
  • 7.Matusheski NV, Caffrey A, Christensen L, Mezgec S, Surendran S, Hjorth MF, et al. Diets, nutrients, genes and the microbiome: recent advances in personalised nutrition. British Journal of Nutrition. 2021;126(10):1489-97.
  • 8. Ülker İ, Ayyıldız F. Artificial Intelligence Applications in
  • Nutrition and Dietetics. JOURNAL OF INTELLIGENT
  • SYSTEMS WITH APPLICATIONS. 2021;4(2).
  • 9. Kelly J, Collins P, McCamley J, Ball L, Roberts S, Campbell K. Digital disruption of dietetics: are we ready? Journal of Human Nutrition and Dietetics. 2021:34(1):134-46 10.Choi RY, Coyner AS, Kalpathy-Cramer J, Chiang MF, Campbell JP. Introduction to machine learning, neural networks, and deep learning. Translational Vision Science & Technology. 2020;9(2):14-.
  • 11. Thomas DM, Kleinberg S, Brown AW, Crow M, Bastian ND, Reisweber N, et al. Machine learning modeling practices to support the principles of Al and ethics in nutrition research. Nutrition & Diabetes. 2022;12(1):48.
  • 12.Oh YJ, Zhang J, Fang M-L, Fukuoka Y. A systematic review of artificial intelligence chatbots for promoting physical activity, healthy diet, and weight loss. International Journal of Behavioral Nutrition and Physical Activity. 2021;18:1-25.
  • 13.Zhang J, Oh YJ, Lange P, Yu Z, Fukuoka Y. Artificial
  • intelligence chatbot behavior change model for designing artificial intelligence chatbots to promote physical activity and a healthy diet. Journal of medical Internet research. 2020;22(9):e22845.14.Iceta S, Tardieu S, Nazare J-A, Dougkas A, Robert M, Disse E. An artificial intelligence-derived tool proposal to ease disordered eating screening in people with obesity. Eating and Weight Disorders-Studies on Anorexia, Bulimia and Obesity. 2021:1-5.15. Allen B, Lane M, Steeves EA, Raynor H. Using Explainable Artificial Intelligence to Discover Interactions in an Ecological Model for Obesity. International Journal of Environmental Research and Public Health. 2022;19(15):9447.16.DeGregory K, Kuiper P, DeSilvio T, Pleuss J, Miller R, Roginski J, et al. A review of machine learningin obesity. Obesity reviews. 2018;19(5):668-85.
  • 17.Sempionatto JR, Montiel VR-V, Vargas E, Teymourian H. Wang J. Wearable and mobile sensors for personalized nutrition. ACS sensors. 2021;6(5): 1745-60.
  • 18.Muzny M. Henriksen A, Giordanengo A. Muzik J, Grøttland A, Blixgård H, et al. Wearable sensors with possibilities for data exchange: Analyzing status and needs of different actors in mobile health monitoring systems. International journal of medical informatics. 2020; 133: 104017.
  • 19.Howard R, Guo J. Hall KD. Imprecision nutrition? Different simultaneous continuous glucose monitors provide discordant meal rankings for incremental postprandial glucose in subjects without diabetes. The American Journal of Clinical Nutrition. 2020;112(4): 1114-9.
  • 20. Kumar P, Sinha R, Shukla P. Artificial intelligence and synthetic biology approaches for human gut microbiome. Critical Reviews in Food Science and Nutrition. 2022;62(8): 2103-21.
  • 21.Kenner B, Chari ST, Kelsen D, Klimstra DS, Pandol SJ,
  • Rosenthal M, et al. Artificial intelligence and early detection of pancreatic cancer: 2020 summative review. Pancreas. 2021;50(3):251. 22.Zeevi D, Korem T, Zmora N, Israeli D, Rothschild D,
  • Weinberger A, et al. Personalized nutrition by prediction of glycemic responses. Cell. 2015;163(5):1079-94. 22.Zeevi D, Korem T, Zmora N, Israeli D, Rothschild D,
  • Weinberger A, et al. Personalized nutrition by prediction of glycemic responses. Cell. 2015;163(5):1079-94. 23.Addanki M, Patra P, Kandra P. Recent advances and
  • applications of artificial intelligence and related technologies in the food industry. Applied Food Research. 2022:100126. 24.Sadhu T, Banerjee I, Lahiri SK, Chakrabarty J. Enhancement of nutritional value of fried fish using an artificial intelligence approach. Environmental Science and Pollution Research. 2021:1-16.
  • 25.Mortazavi BJ, Gutierrez-Osuna R. A review of digital innovations for diet monitoring and precision nutrition. Journal of diabetes science and technology. 2023;17(1):217-23.
  • 26.Zahid M, Sughra U. Development of a protocol for conducting a randomized control trial on effects of artificial intelligence on nutritional status of children post cardiac surgery.
  • JPMA. 2022:72(908).
  • 27.Pekcan AG. Dijital Sağlık: Beslenme ve Diyetetik Bilim
  • Dalında Yaklaşım. Beslenme ve Diyet Dergisi. 2022;50(1):1-6. 28. Petot GJ, Marling C, Sterling L.. An artificial intelligence system for computer-assisted menu planning. Journal of the American Dietetic Association. 1998;98(9): 1009-14.
  • 29.Seljak BK. Computer-based dietary menu planning.
  • Journal of food composition and analysis. 2009;22(5):414-20. 30.Gaál B, Vassányi I, Kozmann G. A novel artificial intelligence method for weekly dietary menu planning. Methods of Information in Medicine. 2005;44(05):655-64.
  • 31.Milne-Ives M. de Cock C. Lim E, Shehadeh MH, de Pennington N, Mole G, et al. The effectiveness of artificial intelligence conversational agents in health care: systematic review, Journal of medical Internet research. 2020;22(10): e20346.
There are 32 citations in total.

Details

Primary Language Turkish
Subjects Artificial Intelligence (Other)
Journal Section Reviews
Authors

Saliha Ersoy 0000-0002-1208-3371

Didem Önay Derin 0000-0003-0624-5714

Publication Date September 2, 2023
Published in Issue Year 2023 Volume: 3 Issue: 2

Cite

Vancouver Ersoy S, Önay Derin D. Yapay Zekanın Beslenme Biliminde Kullanımı. JAIHS. 2023;3(2):9-13.