How Artificial Intelligence is Transforming Nutrition: Here’s what you need to know about AI as a nutrition professional

For nutrition professionals, the advent of AI technology brought a shift in the way they approach, analyze, and interpret nutritional needs. Tools like ChatGPT are now enabling a wide range of tasks, from tailoring grocery lists based on unique dietary needs to suggesting insulin dosing based on blood glucose prediction. Only through understanding the current applications of AI can nutrition professionals adeptly navigate and harness the synergy of nutrition integrated with AI, positioning themselves at the forefront of this transformation.

In this article, we’ll dive into the present and potential future uses of AI in nutrition. 

Understanding Artificial Intelligence

AI is a discipline, much like physics or mathematics, that has evolved significantly in recent years. It is dedicated to making “smart” machines or machines that can think like humans.

• Early AI systems were mainly categorized and labeled. For instance, email spam filters distinguish unwanted email using text and image recognition.
• Newer AI systems can make predictions based on historical data. This type of AI uses machine learning (ML) algorithms to construct models and predict trends. A common application is the “recommended for you” section found on streaming platforms and various marketplaces.
• The latest generation of AI systems can extract meaning from text messages, audio, and video files, as well as provide meaningful responses to inquiries. This type of AI is what enables home assistants to respond to your questions.

How is AI used in Nutrition?

Many food and nutrition organizations use AI for custom nutrition advice, scientific discovery, and monitoring food safety concerns.

Predict Physiological Responses

AI algorithms enable apps to offer personalized food recommendations to individuals with type 2 diabetes to optimize their blood glucose responses. A landmark study in precision nutrition by Zeevi et al. collected data on individuals' blood glucose responses after consuming specific foods and food combinations. These data were then used to develop ML algorithms that predict each person's blood glucose responses based on their dietary choices.

Analyze Bioactive Data

AI algorithms, trained on established bioactive compounds with known DNA sequences and molecular structures, can identify new bioactive compounds in food items such as cranberries, and predict their functions. Collaborating with food companies, this algorithms can facilitate the discovery, extraction, and utilization of new bioactive compounds. For example, a proprietary AI algorithm aided the discovery of N-trans caffeoyl tyramine in hemp hulls, which was predicted to repair gut barrier integrity as seen in inflammatory bowel disease (IBD). This predicted function was then proven in animal research. Moving forward, the potential efficacy of hemp hulls on promoting IBD remission could be tested in clinical trials.

Predict Foodborne Illness Outbreaks

AI technologies, like the Wrap Intelligent Learning EnginE (WILEE) under development by the FDA, can forecast potential food safety hazards in the food system. WILEE integrates, processes, and analyzes data from various sources to identify food safety trends. Once available for use, a potential application could be predicting foodborne illness outbreaks based on data patterns leading up to the outbreak, including a surge in the popularity of a product (e.g., strawberries) and associated pathogens identified in toxicology databases.

Food Label Ingredient Monitoring

The FDA can more effectively perform post-market surveillance on regulation compliance of food ingredients in various food products, including dietary supplements, conventional foods, infant formula, etc. Linking WILEE to FoodTrak (a platform that provides US food label and product sales data) allows monitoring of food labeling ingredient compliance, while prioritizing those with the highest safety concerns (i.e. with higher market share). Linkages of WILEE to FoodTrak can flag commercial products containing high-risk (or banned) ingredients and cross-reference these ingredients with the ingredient dictionary, allowing the FDA to take quicker action.

Simplify Dietary Assessment

Logging meals and completing dietary assessments can be as simple as taking pictures. Classification AI can recognize food from images, estimate portion sizes, and match foods to corresponding nutrition information. Clinical studies are also testing automatic ingestion monitors, which are small cameras mounted on eyeglasses, to automatically identify eating occasions and record meal timing, frequency, speed, and many other dietary parameters. These simplified dietary assessment tools can not only help people track their own intake more effectively, but also could improve the efficiency and accuracy of dietary recall data collection for purposes of research and clinical care.

Accelerating Literature Review

AI-driven tools can aid in literature review by identifying relevant research papers, providing summaries of scientific literature, and highlighting potential areas of interest, thus enhancing the research process. For example, partnering with different publishers and journals, an AI tool can index the content of each publisher, allowing researchers to review scientific literature more effectively.

Personalize Culinary Recommendations

Not sure what to cook for dinner? An AI-powered technology framework can provide recipe recommendations based on a person’s food availability, preferences, cooking skills, and kitchen equipment. Rewire Health’s technology also takes into consideration a person’s medical and nutritional needs when making recommendations.

Ethical Dilemma

The rise of technology trained on extensive data has naturally raised concerns about data privacy and transparency. 

Data privacy

‍AI models are trained on extensive data, which can include sensitive personal information. Hence, ensuring data security is paramount. For example, the electronic medical record (EMR) platform, Epic, and Microsoft have launched a pilot program with UNC Health that uses generative AI to draft responses to patient messages in an effort to improve efficiency of communication. Importantly, providers then can review the draft response by AI or overwrite it with their own. For dietitians, if this AI-powered EMR can be adopted broadly, patients’ request for prescription of supplement and formula can be drafted by AI first, saving much time on administrative tasks. However, since EMRs contain sensitive patient information, allowing another technology company to access and process Epic’s data raises concerns about patient and company data privacy. 

Transparency‍

AI models can make mistakes due to imperfect algorithms, developer oversight, and other factors. Chatbots may mislead users by presenting inaccurate information in a “persuasive and linguistically fluent manner”, and are unable to reference sources. This could confuse consumers without adequate knowledge in the topic. For example, ChatGPT failed to recognize that, once opened, insulin analogs should be stored at room temperature. 

With the growing accessibility of these models, technology companies should transparently disclose the capabilities as well as the limitations of their AI models–and healthcare providers should be aware of such models’ strengths and limitations so that they can accurately advise their patients.

The Bottom Line

As AI technologies become more integrated into the field of nutrition, they offer transformative potential for the practice of nutrition professionals. These advancements provide tools for more convenient dietary assessments, personalized nutrition plans, and comprehensive data analysis. Nutrition professionals who can take advantage of these technological advancement can more effectively provide evidence-based and tailored recommendations for clients. However, alongside these benefits, it’s essential to ensure AI use complements, rather than supplant, expert judgment. In navigating the future of integrating AI with nutrition, nutrition professionals should understand and uphold the personal touch that lie at the heart of their professions.