Artificial Intelligence in Nutrition

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Arti cial Intelligence in Nutrition venkat vajradhar Oct 12, 2019 ¡ 3 min read

People bounce from food to food, nothing to it‌ from the Paleo Diet to the Ketogenic Diet to the Atkins Diet, and then back to where they started. U.S. News has an article on the best foods overall. One day diet is said to solve all of your health problems, but the next day they are fatal. Not only is there a contradiction in the diet, but foods like eggs, coffee, and wine go back and forth between being labeled as healthy and unhealthy. So now people are looking for AI services in Diet plan

Customers are surprised when the results are consistent so that they can finally find a diet that works for them with the help of Artificial Intelligence. But, of course, our bodies are unique so food affects us on an individual level. Unfortunately, most of the studies are observational, based on dietary logic, or the patient’s memory is very inaccurate. To understand the health dynamics of personal


diversity and the implementation of personalized nutrition, efforts should focus on creating attendant practices that monitor the individual’s health responses to food.

To Know about: 6 Amazing AI devices Initially, studies were wrong because of the difficulty in obtaining such accurate and large amounts of data, which prevented researchers from using machine learning. Cardiologist, Eric Topol, participated in the Artificial Intelligence Diet launch earlier this year, to live a long and healthy life. “I participated in a two-week experiment, using a smartphone app to track every morsel I ate, every drink I drank, and every medication I took, as well as how much I slept and exercised.” For more technical purposes, he wears a blood glucose monitor, and researchers analyze the different strains that make up his gut microbiome. Topol received a personalized report card for foods rated from A + (good) to F (bad) based on his analysis. “In the Sweets section: Cheesecake is given an A grade, but whole wheat figs are C — in fruits: strawberries. A +, but grapefruit C. legumes: mixed nuts A +, but Veg burgers are a C. Needless to say, I know healthy food. It didn’t match what I thought. ”Eric Topol His results also include a searchable database on glucose estimates for 100,000 foods and beverages, including specific dietary instructions for preventing glucose spikes.

To Know about: Conversational AI Market: 5 Major Trends (2020) There are many companies in the experimental phase that are exploring the vast capabilities of machine learning about improving existing applications. Some other nutritional studies and applications use deep learning to process photos of the food taken by the participants to know exactly what they are eating to avoid logging the data manually. For example, the application av. Ava offers nutrition and personalized coaching based on a person’s diet and weightrelated chronic conditions. So let’s take a deeper look at how we create this personalized diet…


To implement these techniques in public, many companies are promoting a new science called “nutrigenomics”. Nutrigenomics: a scientific study of nutrition and the interaction of genes, particularly about disease prevention or treatment. — Oxford Dictionary Combining Nutrigenomics and Artificial Intelligence is key to personalized nutrition. This requires filtering large amounts of data. Billions of pieces of data about every person considering all aspects of that person’s lifestyle — health, anatomy, family history, physiology, environment, medical conditions, medication. But, to obtain more accurate results in the future, glucose levels and other anthropometrics such as heart rate and blood pressure responses need to be analyzed using machine learning. Between the technologies we have today, we can do this. We can pull multiple types of data from devices like health apps and smartwatches. For example, Apple owns extensive data from Apple Watches, iPhone apps, and Apple computers. All of this makes customized nutrition very feasible. P.VenkatVajradhar Author

Health

Arti cial Intelligence

Machine Learning

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