An artificial intelligence framework that suggests just one to three ingredient swaps can make meals meaningfully more nutritious and less expensive, according to a new study published May 28 in the open-access journal PLOS Digital Health by Trevor Chan and Ilias Tagkopoulos of the University of California, Davis.
Dietary guidelines that reduce people's risk of conditions like diabetes and cardiovascular disease are well established, but translating nutrition science into day-to-day meals remains difficult for most people. Many diet recommendation tools ask people to change too much as once, leading to unsustainable practices or confusion about how to implement the changes.
In the new study, researchers used data on 135,491 meals logged by 55,228 adults in the What We Eat in America study to identify common meal patterns for breakfast, lunch and dinner. Then, they trained a generative AI model to create realistic meals following those patterns while also adjusting serving sizes. The researchers tested whether the AI could identify one, two, or three ingredient swaps in each meal to further improve nutrition and cost.
Compared to real meals in the same dietary pattern, the AI-generated meals were 47% closer to USDA nutritional targets, while remaining close in their overall meal type and flavors to what people actually eat. When ingredient substitutions were applied, swapping one to three foods improved nutritional quality by approximately 10% while reducing modeled meal costs by 19 to 32%. The most common substitutions identified by the system involved adding vegetables or legumes, and swapping out high-sodium or processed items.
Improving eating habits
Compared to an unspecialized model, GPT-4o, the trained model created meals that were closer to USDA guidelines on macronutrients.
The authors emphasize that the evaluation is entirely computational and has not been tested with real users. However, they suggest that it could help people identify simple ways to improve their eating habits.
"By turning dietary guidelines into realistic, budget-aware meals and simple swaps, this framework can support public-health programs and consumer apps," the authors write.
Chan and Tagkopoulos summarize: "Dietary guidelines often tell people what a healthy diet should look like, but they do not always show how to get there from the meals people already eat. Our study shows that it is possible to translate dietary standards into practical meal-level changes by identifying a small number of ingredient substitutions that can make meals healthier and cost-effective, while keeping them recognizable…[w]hat we found most interesting is that improving meals does not necessarily require a complete redesign. In many cases, targeted substitutions may be enough to move a meal closer to dietary recommendations, which could make healthy eating feel more practical and achievable."
"They add: "Healthier eating does not have to mean giving up the meals people already enjoy. With AI, we can identify small ingredient substitutions that preserve taste, while are better for our health and our pocket."
The work was supported by grants from the U.S. Department of Agriculture and the National Science Foundation.
