In contrast, the AI-integrated dining app often had difficulty accurately identifying the energy content of Asian food mixes, overestimating the calories in beef pho by as much as 49 percent and underestimating the calories in pearl milk tea by up to 76 percent, for example.
“AI-integrated nutrition apps are generally good at detecting individual Western dishes portioned on a plate,” says Dr Cheng from the Charles Perkins Center, “but they often have issues with mixed dishes such as spaghetti Bolognese or burgers. This issue is more prevalent with Asian dishes, which typically contain a variety of mixed ingredients that are not included in the respective app databases, which can lead to errors in calculating the energy content of a particular meal.”
Going forward, the study recommends several steps to improve nutrition apps, including ensuring that the educational content and advice they provide is evidence-based and trustworthy, which can be achieved through collaboration with nutrition experts.
“To make nutrition apps more reliable and accurate, developers should involve nutritionists in their development, train AI models with diverse food images (especially mixed and culturally diverse dishes), expand food ingredient databases, and educate users on how to take high-quality food images for improved recognition accuracy,” Dr Chen said.
“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 and/or consult with a certified dietitian. A dietitian's expertise is invaluable in these cases, as they can provide a more accurate estimate of how much energy your body is consuming and how much energy you most need to achieve an overall healthy diet.”