A new, quickly evolving set of tools uses artificial intelligence to uncover patterns in large amounts of health data, which can subsequently be used to generate forecasts and treatment suggestions. The most well-known of these tools involves image analysis and is starting to be used in therapeutic settings. In addition, Eye Diagnosis recently got FDA approval for image-based AI diagnosis of diabetic retinopathy. Algorithms have been able to identify malignant skin lesions from photos as well as expert dermatologists. AI can also be used for prognostic reasons, such as identifying when trauma patients are about to experience a catastrophic haemorrhage and require rapid treatment, as well as when they are very likely to pass away within a year and should therefore consider switching from traditional care to palliative care.

Also available by AI are counselling recommendations. Finally, although this is a bit contentious, AI algorithms might assist in deciding how to allocate resources. All of these applications call for extremely sizable amounts of health care data, including information on how patients have been treated, how they have responded, and information about the patients themselves, such as genetic information, family history, health-related behavioral, and vital signs. Algorithms cannot be trained or evaluated on their performance after training without these data.

Reference:

//www.ncbi.nlm.nih.gov/pmc/articles/PMC6376961/

The development of AI provides new opportunities for research on nutrients and medical sensing technology. In recent decades, there has been an expansion of AI applications in biomedical sciences. 

ANNs (Artificial Neural Networks) as a currently widely used modeling technique in the field of AI were inspired by the structure of natural neurons of the human brain. Using ANN modeling, significant benefits can be obtained in clinical dietetics. Machine learning techniques are becoming more and more popular in diabetes research: in blood glucose prediction and in the development of the so-called artificial pancreas (a closed-loop system). In the past studies in the field of clinical nutrients research, AI techniques have been used in projects aimed at creating tools supporting dietary activities and in supplementation, as well as in the diagnosis and prediction of the risk of chronic diseases. Fuzzy arithmetic has been used to create “Nutri-Educ”—software for proper balancing of meals, according to the energy needs of the patient. AI techniques also appear to be useful in estimating the risk of health problems based on the analysis of dietary or supplementation patterns. In the area of nutritional epidemiology research, there are identified studies in which advanced AI methods and systems are applied in relation to the dietary assessment, physical monitoring systems and environmental trace elements monitoring systems.

The development of AI systems in dietetics may lead, in the near future, to a partial replacement of medical personnel and reducing the need for personal contact with a nutritionist. In the face of contemporary epidemiological threats, this seems to be of significant importance. The further dynamic development of dietary systems using AI technology may lead to the creation of a global network that will be able to both actively support and monitor the personalized supply of nutrients.(Jaroslaw Sak,2021)

REFERENCE:

Sak J, Suchodolska M. Artificial Intelligence in Nutrients Science Research: A Review. Nutrients. 2021 Jan 22;13(2):322. doi: 10.3390/nu13020322. PMID: 33499405; PMCID: PMC7911928.

References:- 

Mélina Côté and Benoît Lamarche. 2021. Artificial intelligence in nutrition research: perspectives on current and future applications. Applied Physiology, Nutrition, and Metabolism. 47(1): 1-8.  //doi.org/10.1139/apnm-2021-0448

Mélina Côté and Benoît Lamarche. 2021. Artificial intelligence in nutrition research: perspectives on current and future applications. Applied Physiology, Nutrition, and Metabolism. 47(1): 1-8.  //doi.org/10.1139/apnm-2021-0448

M S, Kavita. (2021). APPLICATION OF ARTIFICIAL INTELLIGENCE ON NUTRITION ASSESSMENT AND MANAGEMENT. EUROPEAN JOURNAL OF PHARMACEUTICAL AND MEDICAL RESEARCH. 8. 170-174.

In a recent study called the Nulliparous Pregnancy Outcomes Study: monitoring mothers to be (nuMoM2b), researchers examined the dietary habits of 7572 pregnant women. The participants were asked to complete the Block 2005 Food Frequency Questionnaire, which provided insights into their typical food intake approximately three months before conceiving. The aim of the study was to assess the relationship between fruit and vegetable consumption and the risk of adverse pregnancy outcomes.

To analyze the data, the investigators utilized both multivariable logistic regression and machine learning techniques. The logistic regression model aimed to identify any associations between fruit and vegetable consumption and adverse pregnancy outcomes. However, it did not find any significant links between the two.

On the other hand, the machine learning model revealed interesting findings. It showed that women who were in the top 20% (≥ 80th percentile) in terms of total fruit or vegetable density consumption had a slightly lower incidence of preterm birth, small-for-gestational-age births, gestational diabetes, and pre-eclampsia. This suggests that high consumption of fruits and vegetables may be associated with a reduced risk of these adverse pregnancy outcomes.

Overall, this study highlights the potential benefits of incorporating a diet rich in fruits and vegetables during pregnancy. By using advanced analytical techniques like machine learning, researchers were able to uncover associations that were not initially apparent through traditional logistic regression analysis.

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A machine-learning algorithm that monitors food preferences and makes nutritious recipe suggestions tailored to each individual’s needs has been devised in New York. The programme notes personal likes and dislikes, allergies and other factors to guide healthy eating.

Applications of Artificial Intelligence (AI) in Nutrition Informatics are revolutionizing the field, enabling personalized nutrition recommendations, food recognition and analysis, disease risk assessment, and nutrient analysis and dietary planning. AI algorithms can analyze vast amounts of patient data to generate tailored dietary plans and guidelines based on individual characteristics. Computer vision and deep learning techniques enable AI systems to automatically recognize and analyze food items from images or videos, facilitating accurate dietary tracking. By processing electronic health records, genetic information, and lifestyle factors, AI algorithms assess an individual's risk of nutrition-related diseases, allowing for early detection and intervention. Additionally, AI technologies automate nutrient analysis and create optimized dietary plans based on specific nutritional requirements, considering factors such as allergies, restrictions, and goals. These applications of AI in nutrition informatics enhance patient care, enable personalized interventions, and advance research capabilities.

References:

1. Vyas, A., Kumar, V., & Khan, M. (2021). Artificial intelligence in nutritional informatics: current trends, challenges, and future perspectives. Current Opinion in Food Science, 39, 64-70. Link

2. Zhang, C., & Zhu, S. (2018). Artificial intelligence in nutrition. Current Opinion in Clinical Nutrition and Metabolic Care, 21(6), 430-435. Link

3. Özdemir, V., & Patrinos, G. P. (2019). Artificial intelligence in precision health: AI-powered telemedicine, wearable sensors, and smart devices. Human Genomics, 13(1), 1-9. Link

4. Escobar-Molina, R., Vilar, Z., & Vargas-Quesada, B. (2021). Applications of artificial intelligence and machine learning in health sciences research. Frontiers in Big Data, 4, 26. Link

5.  

Artificial intelligence (AI) is an everyday part of our daily lives although many of us do not recognize it.

AI algorithms might help to predict the connections between nutrition and health, leading to improved dietary assessment especially with respect to self-reporting errors.

Artificial intelligence (AI) has significant applications in the field of Nutrition Informatics. AI can be applied in multidisciplinary fields including patient service and care. It enables precise and personalized medical nutrition care by assessing food and nutrient intake, nutritional evaluation. The application of AI for the provision of food services to hospitalized patients is of immense scope.

AI automates the process of assessing dietary intake by analyzing food images or descriptions improving accuracy and convenience. AI-powered chatbots and applications also provide personalized coaching and support for behavior change and dietary adherence.

AI has the potential to revolutionise Nutrition Informatics by leveraging data-driven approaches to improve dietary assessment, personalized nutrition, disease management, agriculture practices, behavior change and food safety. These applications can empower individuals, healthcare professionals and the food industry to make informed decisions for better health outcomes.

Reference:
1. //www.researchgate.net/publication/
2. //journals.lww.com/acsm-healthfitness/Fulltext/2022/05000/Artificial_Intelligence_and_Precision_Nutrition.12.aspx

Artificial intelligence can be used in a variety of interdisciplinary fields, such as healthcare. By evaluating nutritional status and measuring food and nutrient consumption, it provides precise and individualised medical nutrition management to the patients.Reliable and accurate food and nutrient intake data are essential to plan and assess the effect of therapeutic menus for a patient under medical care. Earlier studies reported that the reliability of the data obtained through traditional methods might be biased due to incorrect estimation of the food intake data. Moreover, the data does not provide any evidence or truthfulness of the menu consumed. AI can play a significant role in providing high-quality patient care and service through food and nutrition.

AI for nutrition informatics can be used in many ways.For recipe standardization, datasets of images of food ingredients can be used. For the elderly and patients webcams can be placed above plate placement. The image taken by the system will be analyzed by three stages such as segmentation, recognition, and estimation of portion size. Algorithms developed based on the data sets such as food and ingredient images, nutrition information from food labels, and nutrient composition databases enable the nutritional analysis of the meal.

AI can be applied in various ways to assess and manage nutrition. It enables precise and personalized medical nutrition care by assessing food and nutrient intake and evaluating nutritional adequacy. AI-based systems can be used to provide food services to hospitalized patients, allowing for accurate monitoring of nutrient intake without direct contact. These systems can also assist in the evaluation of dietary intake, helping to improve clinical outcomes and manage conditions such as obesity and diet-related diseases.

However, it is important to note that further research and validation are needed in this field, as many commercially available AI-based nutritional assessment systems have not been adequately validated.

Reference:

//www.researchgate.net/publication/352091323_APPLICATION_OF_ARTIFICIAL_INTELLIGENCE_ON_NUTRITION_ASSESSMENT_AND_MANAGEMENT

Artificial intelligence (AI) has made significant contributions to various fields, including nutrition informatics. By leveraging AI techniques such as machine learning, deep learning, and natural language processing, researchers and practitioners in nutrition informatics have been able to develop innovative applications that enhance nutritional assessment, personalized recommendations, and dietary monitoring. Here are some notable applications of AI in nutrition informatics:

• AI can assist in dietary assessment by automating the analysis of food intake data. This includes image recognition and analysis of food images, text mining of food diaries, and automatic food logging. AI algorithms can accurately identify and quantify food items, portion sizes, and nutrient content from various data sources.
• AI can enable personalized nutrition recommendations by analyzing individual characteristics, such as genetics, health status, dietary preferences, and lifestyle factors. Machine learning algorithms can integrate these data points to develop personalized dietary plans and recommend optimal food choices for individuals.
• AI can aid in disease prediction and management by analyzing large-scale health data and identifying patterns, risk factors, and early warning signs. Machine learning algorithms can analyze electronic health records, genomics data, and lifestyle factors to predict the onset of diseases, such as diabetes, cardiovascular diseases, and cancer.
• AI can assist in generating evidence-based nutritional recommendations by analyzing large-scale datasets, scientific literature, and clinical guidelines. Machine learning algorithms can identify associations between dietary factors and health outcomes, enabling the development of more accurate and personalized nutritional guidelines.

References:

• Mhanna, M. J., & Crichton, G. E. (2019). The use of artificial intelligence in dietary assessment: Opportunities and challenges. Current nutrition reports, 8(4), 307-316.
• Zhao, L., Zhang, X., Shen, Y., Fang, Z., Wang, Y., Hu, J., & Xie, X. (2020). Personalized nutrition recommendation algorithm based on user preferences and food composition data. Journal of the American Medical Informatics Association, 27(7), 1114-1120.
• Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., & Yang, G. Z. (2017). Deep learning for health informatics. IEEE journal of biomedical and health informatics, 21(1), 4-21.
• Shin, D., Lee, J., You, S., Lee, S., & Hong, J. (2018). Using machine learning approaches for dietary pattern analysis: a systematic review. Nutrition research and practice, 12(6), 475-488.

AI has the potential to revolutionize nutrition informatics by enhancing data processing, pattern recognition, and decision-making support. Key applications of AI in nutrition informatics include automating dietary assessment, offering personalized nutrition recommendations, analyzing recipes and food composition, aiding in disease management, providing nutritional education and behavior change interventions, optimizing agriculture practices, and ensuring food safety and quality control.

A famous AI-powered health and nutrition app from India is called Healthify. It analyses user data and offers individualised nutrition advice using AI and machine learning techniques. Users can get personalised meal plans based on their objectives and interests, track their dietary intake, and receive nutritional analyses.Traditional methods, such as pamphlets and paper drawings, cause information loss, inability to monitor patient status, and time-consuming repetition.

Artificial intelligence (AI) in nutrition is increasing individualised dietary counselling and evaluation. AI makes it possible to identify population subgroups and those who might profit from revised dietary advice. Deep learning and other AI-based technologies are increasing the precision of nutritional consumption calculations and food image identification. The objective quantification of food intake is made possible by deep learning algorithms, which have showed promise in the recognition and classification of food items from photographs. With the help of these developments in AI, personalised dietary advice based on aspects like genetic, epigenetic, metabolic, and gut microbial characteristics will be possible. This will help us better understand inter-individual variability. Such innovations could improve the way we evaluate food.

Reference :

1.Kevin B. Johnson et al , Precision Medicine, AI, and the Future of Personalized Health Care,ASCPT, 2021. //www.ncbi.nlm.nih.gov/pmc/articles/PMC7877825/

2.Mohammad Aerry Asmani Md Nizam et al, Diabetic Care Management System to Improve Dietitian-Patient Consultation Process,IEEE Control and System Graduate Research Colloquium (ICSGRC), conference, 2021. //ieeexplore.ieee.org/document/9515229

3.Nathan V Matusheski et al, Diets, nutrients, genes and the microbiome: recent advances in personalised nutrition, Br. J nutrition, 2021. //www.ncbi.nlm.nih.gov/pmc/articles/PMC8524424/  

AI technology is becoming increasingly important in everyday activities, including medical domains. With the increasing need for healthcare, hospital requirements are shifting from informed networking to the Internet Hospital and, eventually, to the Smart Hospital.AI algorithms may aid in better understanding and prediction of the complicated and nonlinear relationships between nutrition-related data and health outcomes, especially when huge amounts of data must be processed and integrated, as in metabolomics.

AI has numerous applications in public health. One traditional application is to detect disease outbreaks using search engine query data or social media data, as Google performed for influenza epidemic prediction and the Chinese Academy of Sciences did for modeling the COVID-19 outbreak using multi-source information fusion.

AI-based methods, such as picture recognition, may help improve dietary assessment by increasing efficiency and eliminating systematic and random mistakes in self-reported dietary intake assessments. High-dimensional data, such as multi-omics data, patient characteristics, medical laboratory test data, and so on, are frequently utilized to generate various predictive or prognostic models using machine learning or statistical modeling approaches. Image of a doctor As there are multiple models for classification, detection, and segmentation tasks in CV, AI is one of the most developed mature domains. CV algorithms can also be utilized in clinical settings for computer-aided diagnosis and treatment using ECG, CT, eye fundus imaging, and so on. While human doctors may become fatigued and prone to errors after examining hundreds of photographs for diagnosis, AI doctors can surpass a human medical image viewer because of their expertise in repetitive labor without weariness.

AliveCor has created an algorithm for wearable devices that can automatically anticipate the existence of atrial fibrillation, which is an early warning indication of stroke and heart failure. The 23andMe company may also analyze saliva samples for a low cost and provide customers with information based on their genes, such as who their ancestors were or potential diseases they may be prone to later in life. It provides precise health management solutions based on genetic data from individuals and families.

Many elements of drug development have embraced AI, including de novo molecule creation, structure-based modeling for proteins and ligands, quantitative structure-activity connection research, and druggable property judgments. In addressing some difficult drug discovery problems, DL-based AI appliances outperform.

AI programs can gather, organize, and analyze enormous amounts of data from social media sites to better understand the population's food habits and attitudes. In conclusion, AI-based approaches are likely to improve and expand nutrition research, as well as aid in the exploration of new applications. However, more research is needed to discover areas where AI can provide extra value over traditional approaches, as well as areas where AI is unlikely to advance the subject.

references:-

1. Xu, Yongjun et al. “Artificial intelligence: A powerful paradigm for scientific research.” Innovation (Cambridge (Mass.)) vol. 2,4 100179. 28 Oct. 2021, doi:10.1016/j.xinn.2021.100179

2. //doi.org/10.1139/apnm-2021-0448

As it is mentioned in the topic starter, Solution to the complexity  non linear relationship between the nutritional data and health outcomes is having greater importance as the community- based dietary assessments/interventions require tailored system emphasizing an individual  without compromising accuracy.

There are many challenges in administering self reporting tools such as 3 day food records, 24-h recall or food frequency questionnaire as it has potential bias(İzzet Ülker & Feride Ayyıldız, 2021).

With the support of ML ,AI can solve this problem by processing complex data to generate results relevant to health and disease or to process large volumes of complex data—in other words, to apply these algorithms to a very specific scope of the task(Kirk et al., 2022).

Application of advanced High -dimensional MI over low-dimensional, traditional statistical techniques such as regression methods may suffice will be helpful in the field. Reinforcement learning type of MI  may be  helpful for individual level of tailoring the nutrition informatics.

Mechanism of  Reinforcement learning MI:

The algorithm exists in a dynamic environment and is penalized or rewarded for the decisions that it makes within the environment. The algorithm then updates its behavior to maximize reward, minimize penalization, or both. This allows the algorithm to become proficient in a task without being explicitly programmed to behave in a certain way.  The complex nature of reinforcement learning is useful where  the integration of complex and varied data is concerned, such as recommender systems  or mobile-based fitness app(Kirk et al., 2022).

The concept of xAI(Expainable AI) is concerned with not only generating an output but also how it was generated. The results of xAI can be informative in that they reveal which features contributed most to the algorithm output. This is understandable in medical situations where the predicting output can have serious consequences for, say, patient lifestyle or treatment avenues(Alejandro Barredo Arrieta et al., 2020).

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//www.ncbi.nlm.nih.gov/pmc/articles/PMC9776646/

//www.sciencedirect.com/science/article/abs/pii/S1566253519308103

Artificial intelligence (AI), a branch of computer science with the goal of mimicking thought processes, learning capacities, and knowledge management, is finding increasing uses in experimental and clinical medicine. AI applications in biomedical sciences have grown in popularity in recent decades. The capabilities of artificial intelligence in medical diagnostics, risk prediction, and therapeutic approach assistance are quickly expanding. The purpose of this article is to examine the present use of artificial intelligence in nutrition science research. The development of dietary systems employing AI technology may result in the establishment of a global network capable of actively supporting and monitoring the personalised delivery of nutrients.

References:

//www.ncbi.nlm.nih.gov/pmc/articles/PMC7911928/