AI Food Tracking Apps Need Improvement to Address Accuracy, Cultural Diversity

In contrast, AI-integrated food apps often had difficulty accurately identifying energy content for mixed Asian dishes, for example the calories for beef pho were overestimated by 49 percent, while pearl milk tea had calorie underestimations of up to 76 percent.

“Nutrition apps with AI-integration are generally better at detecting individual Western foods when they are separated on a plate,” said Dr Chen who is also from the Charles Perkins Centre. “However, they often struggle with mixed dishes, such as spaghetti bolognese or hamburgers. This issue is more common with Asian dishes, which usually contain a variety of mixed components that may not be found in the respective apps database, leading to possible errors when calculating the energy amount of a particular meal.”

Moving forward, the study recommends several steps for the improvement of nutrition apps. This includes ensuring that the educational content and advice provided by the apps are evidence-based and trustworthy, which can be achieved through collaboration with nutrition experts.

"To enhance the credibility and accuracy of nutrition apps, creators should engage dietitians in their development, train AI models with diverse food images – particularly for mixed and culturally varied dishes – expand food composition databases and educate users on capturing high-quality food images for better recognition accuracy,” said Dr Chen.

"If you're monitoring your health, such as managing high blood pressure or tracking your sodium intake, it's important to compare your food choices with nutrition labels or consult with an accredited practicing dietitian. A dietitian's expertise is invaluable in these cases, as they can provide more accurate estimates of how much energy your body is consuming and what it requires most to achieve a holistically healthy diet.”