Smartphone Apps for Tracking Food Consumption and Recommendations: Evaluating Artificial Intelligence-based Functionalities, Features and Quality of Current Apps
Sabiha Samad, Fahmida Ahmed, Samsun Naher, Muhammad Ashad Kabir, Anik, Das, Sumaiya Amin, Sheikh Mohammed Shariful Islam

TL;DR
This study evaluates the quality and AI functionalities of food tracking and recommendation apps, revealing most lack essential features and proposing design improvements for future development.
Contribution
It introduces an evaluation tool for assessing AI-based functionalities and quality in food apps, providing insights and guidelines for better app design.
Findings
Most apps lack comprehensive AI features for food recognition.
'Foodvisor' uniquely offers automatic food recognition and nutritional computation.
Current apps need significant improvements in automation and AI functionalities.
Abstract
The advancement of artificial intelligence (AI) and the significant growth in the use of food consumption tracking and recommendation-related apps in the app stores have created a need for an evaluation system, as minimal information is available about the evidence-based quality and technological advancement of these apps. Electronic searches were conducted across three major app stores and the selected apps were evaluated by three independent raters. A total of 473 apps were found and 80 of them were selected for review based on inclusion and exclusion criteria. An app rating tool is devised to evaluate the selected apps. Our rating tool assesses the apps' essential features, AI-based advanced functionalities, and software quality characteristics required for food consumption tracking and recommendations, as well as their usefulness to general users. Users' comments from the app stores…
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