Artificial intelligence assisted food science and nutrition perspective for smart nutrition research and healthcare

被引:13
作者
Joshi, Saloni [1 ]
Bisht, Bhawna [1 ]
Kumar, Vinod [1 ]
Singh, Narpinder [1 ]
Pasha, Shabaaz Begum Jameel [2 ]
Singh, Nardev [3 ]
Kumar, Sanjay [1 ]
机构
[1] Graph Era Deemed Univ, Dept Food Sci & Technol, Dehra Dun 248002, Uttarakhand, India
[2] Graph Era Deemed Univ, Dept Microbiol, Dehra Dun 248002, Uttarakhand, India
[3] Graph Era Hill Univ, Sch Pharm, Dehra Dun 248002, Uttarakhand, India
来源
SYSTEMS MICROBIOLOGY AND BIOMANUFACTURING | 2024年 / 4卷 / 01期
关键词
Artificial intelligence; Machine learning; Nutrition; Healthcare; Neural networks; NEURAL-NETWORK; BIG DATA; RECOGNITION; EXTRACTION; ALGORITHM; LEGAL;
D O I
10.1007/s43393-023-00200-4
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
Artificial Intelligence (AI) has the potential to dramatically change the field of healthcare and nutrition by imitating human cognitive processes. This field involves smart machine-based applications, such as Machine Learning (ML), neural networks, and natural language processing to tackle and solve various issues. The current study's purpose is to highlight specific AI-based applications that are currently being employed in the fields of nutrition and healthcare. The published data from various search engines, such as PubMed/Medline, Google Scholar, Scopus, Web of Science, and Science Direct, were used for collecting the relevant data. The study depicts that there are several AI-based approaches and methods available for by improving diagnosis and treatment, lowering costs, and increasing access to healthcare facilities. Although AI cannot replace the personal touch, empathy, and emotional support provided by healthcare professionals. These approach assistances expanding rapidly are of great use. However, it is crucial to be careful and make sure that moral considerations are given top priority.
引用
收藏
页码:86 / 101
页数:16
相关论文
共 120 条
[21]   Rethinking the Mobile Food Journal: Exploring Opportunities for Lightweight Photo-Based Capture [J].
Cordeiro, Felicia ;
Bales, Elizabeth ;
Cherry, Erin ;
Fogarty, James .
CHI 2015: PROCEEDINGS OF THE 33RD ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, 2015, :3207-3216
[22]   Artificial intelligence in nutrition research: perspectives on current and future applications [J].
Cote, Melina ;
Lamarche, Benoit .
APPLIED PHYSIOLOGY NUTRITION AND METABOLISM, 2022, 47 (01) :1-8
[23]   Applying Image-Based Food-Recognition Systems on Dietary Assessment: A Systematic Review [J].
Dalakleidi, Kalliopi, V ;
Papadelli, Marina ;
Kapolos, Ioannis ;
Papadimitriou, Konstantinos .
ADVANCES IN NUTRITION, 2022, 13 (06) :2590-2619
[24]   Transforming Diabetes Care Through Artificial Intelligence: The Future Is Here [J].
Dankwa-Mullan, Irene ;
Rivo, Marc ;
Sepulveda, Marisol ;
Park, Yoonyoung ;
Snowdon, Jane ;
Rhee, Kyu .
POPULATION HEALTH MANAGEMENT, 2019, 22 (03) :229-242
[25]  
Davenport Thomas, 2019, Future Healthc J, V6, P94, DOI 10.7861/futurehosp.6-2-94
[26]  
Demirci F, 2016, AM J CLIN PATHOL, V146, P227, DOI [10.1093/AJCP/AQW104, 10.1093/ajcp/aqw104]
[27]   Artificial intelligence in clinical and genomic diagnostics [J].
Dias, Raquel ;
Torkamani, Ali .
GENOME MEDICINE, 2019, 11 (01)
[28]   Ensemble Deep Learning Derived from Transfer Learning for Classification of COVID-19 Patients on Hybrid Deep-Learning-Based Lung Segmentation: A Data Augmentation and Balancing Framework [J].
Dubey, Arun Kumar ;
Chabert, Gian Luca ;
Carriero, Alessandro ;
Pasche, Alessio ;
Danna, Pietro S. C. ;
Agarwal, Sushant ;
Mohanty, Lopamudra ;
Sharma, Neeraj ;
Yadav, Sarita ;
Jain, Achin ;
Kumar, Ashish ;
Kalra, Mannudeep K. ;
Sobel, David W. ;
Laird, John R. ;
Singh, Inder M. ;
Singh, Narpinder ;
Tsoulfas, George ;
Fouda, Mostafa M. ;
Alizad, Azra ;
Kitas, George D. ;
Khanna, Narendra N. ;
Viskovic, Klaudija ;
Kukuljan, Melita ;
Al-Maini, Mustafa ;
El-Baz, Ayman ;
Saba, Luca ;
Suri, Jasjit S. .
DIAGNOSTICS, 2023, 13 (11)
[29]   Predicting nationwide obesity from food sales using machine learning [J].
Dunstan, Jocelyn ;
Aguirre, Marcela ;
Bastias, Magdalena ;
Nau, Claudia ;
Glass, Thomas A. ;
Tobar, Felipe .
HEALTH INFORMATICS JOURNAL, 2020, 26 (01) :652-663
[30]   Diagnostic performance of an artificial neural network to predict excess body fat in children [J].
Duran, Ibrahim ;
Martakis, Kyriakos ;
Rehberg, Mirko ;
Semler, Oliver ;
Schoenau, Eckhard .
PEDIATRIC OBESITY, 2019, 14 (02)