About this Research Topic
Dietary assessment plays a crucial role in promoting individual and public health by evaluating nutritional intake and its impact on well-being. Traditional methods of dietary assessment, such as self-reported surveys and food diaries, are subjective, time-consuming, and prone to inaccuracies. In recent years, advancements in artificial intelligence (AI) have opened new avenues for transforming dietary assessment. AI-based approaches, including computer vision, natural language processing, and machine learning algorithms, offer the potential to automate and enhance the accuracy, efficiency, and objectivity of dietary assessment.
The application of AI in dietary assessment has gained significant attention across various disciplines, including nutrition, public health, computer science, and data analytics. Multiple studies have explored AI algorithms to identify and classify food items, estimate portion sizes, assess nutritional content, and track dietary patterns. However, this field is still in its early stages, and there are several challenges to address, such as standardizing data collection protocols, ensuring accuracy across diverse populations and cultures, and integrating AI technology into practical dietary assessment tools.
The goal of this Research Topic is to provide a comprehensive overview of the current state-of-the-art in AI-based dietary assessment, focusing on innovative approaches, challenges, and future directions. This Research Topic aims to bring together researchers, practitioners, and policymakers from diverse disciplines to share their expertise, discuss recent advancements, and identify potential collaborations.
By exploring the latest research and developments in AI-based dietary assessment, we aim to promote a deeper understanding of the opportunities and challenges associated with these technologies. Additionally, this Research Topic will highlight the potential of AI to revolutionize dietary assessment methods, improve accuracy, and enhance public health interventions.
Topics of interest include, but are not limited to, the following:
- AI algorithms for food recognition and portion size estimation
- AI-based dietary assessment methods
- Integration of multiple data sources (e.g. images, text, speech) for comprehensive dietary assessment
- Food recommendation system
- AI approaches for dietary pattern analysis and nutritional profiling
- Privacy, security, and ethical considerations in AI-based dietary assessment
- AI applications for personalized nutrition recommendations and interventions
- Benchmark dataset for large-scale food analysis
- Other multimedia computing technologies and applications for food-related purposes
The application of AI in dietary assessment has gained significant attention across various disciplines, including nutrition, public health, computer science, and data analytics. Multiple studies have explored AI algorithms to identify and classify food items, estimate portion sizes, assess nutritional content, and track dietary patterns. However, this field is still in its early stages, and there are several challenges to address, such as standardizing data collection protocols, ensuring accuracy across diverse populations and cultures, and integrating AI technology into practical dietary assessment tools.
The goal of this Research Topic is to provide a comprehensive overview of the current state-of-the-art in AI-based dietary assessment, focusing on innovative approaches, challenges, and future directions. This Research Topic aims to bring together researchers, practitioners, and policymakers from diverse disciplines to share their expertise, discuss recent advancements, and identify potential collaborations.
By exploring the latest research and developments in AI-based dietary assessment, we aim to promote a deeper understanding of the opportunities and challenges associated with these technologies. Additionally, this Research Topic will highlight the potential of AI to revolutionize dietary assessment methods, improve accuracy, and enhance public health interventions.
Topics of interest include, but are not limited to, the following:
- AI algorithms for food recognition and portion size estimation
- AI-based dietary assessment methods
- Integration of multiple data sources (e.g. images, text, speech) for comprehensive dietary assessment
- Food recommendation system
- AI approaches for dietary pattern analysis and nutritional profiling
- Privacy, security, and ethical considerations in AI-based dietary assessment
- AI applications for personalized nutrition recommendations and interventions
- Benchmark dataset for large-scale food analysis
- Other multimedia computing technologies and applications for food-related purposes
Keywords: artificial intelligence, dietary assessment, portion size estimation, dietary monitoring, food recognition
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