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Artificial intelligence (AI) is transforming the way we perceive and manage nutrition. There are applications for diet tracking, which offer personalized guidance and meal plans, solutions that pinpoint ingredients with specific health benefits, and tools for analyzing medical data to inform customized nutrition interventions.

These technologies serve to optimize medical outcomes, improve public health nutrition advice, encourage healthy eating, support chronic disease management, prevent health decline, aid disease prevention, and improve overall well-being.

The use of AI and machine learning (ML) in nutrition has benefits in several areas, including:

A one-size-fits-all approach to public health nutrition guidance fails to account for different dietary preferences, health goals, lifestyles, nutritional requirements, intolerances, allergies, and other health conditions.

A young and active vegan with a nut allergy, for example, has hugely different dietary needs to an elderly carnivore living with diabetes.

AI-powered technology can quickly analyze vast amounts of nutrition data and cross-reference it with an individuals measurements and requirements to produce personalized and optimal nutrition plans for all.

Clinical nutrition can be defined as a discipline that deals with the prevention, diagnosis, and management of nutritional and metabolic changes related to acute and chronic disease and conditions caused by a lack or excess of energy and nutrients.

AI has several applications in this field, from analyzing complex medical data and medical images to informing the decisions of medical practitioners and producing personalized nutrition plans for patients. Because AI solutions can identify previously overlooked associations between diet and medical outcomes, they can improve chronic disease management, optimize patient recovery, and improve patient wellbeing.

A tailored nutrition plan for a diabetic person, for example, will evaluate their gut microbiome and blood glucose levels, while a person with cardiovascular problems may require a diet that takes into consideration their cholesterol levels and blood pressure.

There has been a rise in AI-powered apps that assist users in tracking their nutritional intake while offering personalized guidance on making healthier choices.

The challenge with self-reported food diaries is that they depend on the memory and honesty of individuals, which often leads to under- and over-reporting and other inaccuracies. When certain snacks and meals are forgotten, portion sizes are miscalculated, or food choices that are perceived to be less healthy are deliberately omitted, it is more difficult for nutrition-focused apps and healthcare professionals to provide informed and effective nutritional advice.

With AI-powered computer vision technology, food tracking apps can identify food items, estimate portion sizes, and calculate nutritional values with increasing accuracy. Coupled with wearable devices, which track a users activity, this technology is empowering people to make optimal nutritional choices. Some nutrition apps offer additional personalization.

For example, they might partner with health organizations to obtain their users electronic health records or feature a nutrition chatbot to quickly respond to queries or perform a dietary assessment.

Nutraceuticals are products derived from food sources that promise additional health benefits to their basic nutritional value. Some examples include glucosamine, which is used in the treatment of arthritis; omega-3 fatty acids, which are used to treat inflammatory conditions; and many nutrient-rich foods, including soybeans, ginger, garlic, and citrus fruits.

Various nutraceutical companies have come under fire for marketing products as health solutions without meaningful scientific evidence to back their claims. But AI looks set to transform the industrys image by finding genuine health solutions fast.

The speed and accuracy with which an AI solution can identify bioactive compounds in foods and then predict the actions they will have in the body is of particular interest to nutraceutical companies. At present, it often takes several years to identify, develop, test, and launch a new ingredient.

In the future, ML solutions are likely to support the development of targeted nutraceutical solutions.

Across 48 countries, 238 million people are facing high levels of acute food insecurity. Meanwhile, one-third of the food produced for human consumption is lost or wasted, which equates to 1.3 billion tons every year.

AI is aiding the global effort to address food insecurity and reduce waste generation.

It can predict demand for certain crops to enable farmers to optimize their planting plans, detect crop and livestock disease at an early stage to contain damage and limit loss, and identify trends in consumer behavior to help retailers forecast demand and better manage their inventories. In addition, AI systems can track food from farm to plate, helping to ensure is it harvested, shipped, and consumed on time.

In the aftermath of a natural disaster or conflict, AI can quickly analyze data to inform humanitarian responses.

The challenges associated with AI in nutrition include:

To improve accuracy and efficiency, ML solutions are fed vast amounts of training data. In nutrition, such data is especially sensitive, including personal information and medical records.

Once a product, such as a food tracking app, is live, additional data is collected, as users are required to disclose personal information, including measurements, medications, food intake, and existing health conditions.

Rigorous safeguarding must be implemented to ensure that all personal data is safely collected and stored and that users understand how it is being used.

AI solutions are known to perpetuate societal stereotypes and biases. Amazon deployed a recruitment system that discriminated against women, the Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) created a tool to predict the likelihood of criminals reoffending, which misclassified almost twice as many Black defendants, and a Twitter algorithm was proven to favor white faces over Black faces when cropping images.

If nutrition-centered AI solutions are not carefully developed, these tools could reinforce outdated and oversimplified concepts of nutritional health and wellness or reflect biases in the healthcare system.

The use of diverse training data can prevent unfair or inaccurate outcomes, and these tools must be continuously monitored and updated to echo the latest healthcare guidance.

Meal scanning technology enables food-tracking app users to log their intake by simply snapping a photograph of their meals via their cell phone cameras. These tools are exceptionally fast and can be highly accurate, but there are some major limitations to consider.

For example, the technology will struggle to detect a basic ingredient swap in familiar recipes. When scanning a slice of cake, it would record items such as butter and eggs, even if those ingredients had been replaced with avocado and yogurt. Similarly, the app wont register when a creamy pasta sauce is replaced with a milk-based alternative.

Fortunately, these shortcomings can be addressed with some manual effort on the users part.

Nutrition is a complex and nuanced field, which will continue to benefit from the inputs and expertise of qualified healthcare professionals.

Take the management of chronic illnesses as an example. While an AI-powered app can produce highly customized dietary plans for individuals living with diabetes, celiac disease, or Crohns disease, additional medical support and monitoring is likely to be required.

Complexities also arise when poor or unusual eating habits are linked to mental health conditions, such as eating disorders. In these scenarios, food-tracking apps are likely to cause more harm than good.

The market for personalized nutrition is fast-expanding, driven largely by rapid developments in AI.

Some exciting industry players include;

Nutrition labels are designed to prevent false advertising and promote food safety. But perhaps one of the most arduous tasks involved in launching new food products, medicines, and supplements is ensuring adherence to labeling regulations and standards, which are not only complex but can also vary enormously from country to country. Manual reviews in the food industry are repetitive, slow, and prone to human error, which, at best, results in delayed product launches and, at worst, poses a threat to human health.

Verifying the accuracy and compliance of labels is made easy with AI algorithms. Manufacturers simply upload their recipes and packaging design to an AI-powered tool, which analyzes the ingredients and identifies any issues. This drives operational efficiencies, reduces product waste, ensures customer safety, and enables more cost-effective international trade.

An increasing number of companies are using smart labels to provide consumers with additional nutritional information. This enables people to make more informed decisions about the food and supplements they consume while ensuring food safety.

The growing demand for personalized nutrition has led to the rapid adoption of fitness-tracking apps like MyFitnessPal and MyPlate. Indeed, almost two-thirds of American adults are mobile health app users, according to a 2023 survey.

Amid widespread criticism that these apps are promoting unhealthy diets, extreme exercise regimes, and rapid weight loss, users must understand the technologys limitations.

Here are some important things to consider:

AI-powered apps are more likely to be beneficial when healthcare professionals, including doctors and dietitians, work closely with their patients and clients to recommend appropriate products and monitor usage.

The applications of AI in nutrition are far-reaching, enabling personalized diet plans, enhanced clinical nutrition, the development of targeted nutraceuticals, and more effective methods for addressing food insecurity.

As adoption increases, these solutions will require increasingly robust regulation, particularly in relation to data handling and security, algorithm bias, and consumer education.

With the revenue from health apps forecast to grow to $35.7 billion by 2030, healthcare professionals must be aware of the information that is being communicated to consumers so they can guide their patients toward truly health-promoting options.

As for the developers of AI-powered nutrition technology, inputs from experts in diverse fields, including healthcare, nutrition, technology, and ethics, will ensure solutions are safe and effective.

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Artificial Intelligence in Nutrition: Definition, Benefits, and Algorithms - ThomasNet News