McCarthy Danielle I
Queen's University Belfast, Belfast, UK.
Spoon Guru, London, UK.
Nutr Bull. 2025 Mar;50(1):142-150. doi: 10.1111/nbu.12729. Epub 2025 Jan 12.
Transformative change is needed across the food system to improve health and environmental outcomes. As food, nutrition, environmental and health data are generated beyond human scale, there is an opportunity for technological tools to support multifactorial, integrated, scalable approaches to address the complexities of dietary behaviour change. Responsible technology could act as a mechanistic conduit between research, policy, industry and society, enabling timely, informed decision making and action by all stakeholders across the food system. Domain expertise in food, nutrition and health should always be integrated into both the development and continuous deployment of AI-powered nutritional intelligence (NI) to ensure it is responsible, accurate, safe, useable and effective. Dietary behaviours are complex and improving diet-related health outcomes requires socio-cultural-demographic considerations within the design and deployment of NI tools. This article describes existing examples of NI within the food system and future opportunities. Human-in-the-loop approaches with food, health and nutrition experts involved at all stages including data acquisition, processing, output validation and ongoing quality assurance are essential to ensure evidence-based practice. The same ethical considerations should apply in this domain as in any other (e.g. privacy, inclusivity, robustness, transparency and accountability) and responsible practice must encompass rigorous standards and alignment with regulatory frameworks. Critical today and in the future is accessibility to appropriate high-quality food compositional data sets, which include up-to-date information on commercially available products that reflect the constantly evolving food landscape to realise the potential of responsible AI to help address the existing food system challenges.
整个食品系统需要进行变革性改变，以改善健康和环境状况。由于食品、营养、环境和健康数据的产生超出了人类规模，技术工具便有机会支持多因素、综合、可扩展的方法，以应对饮食行为改变的复杂性。负责任的技术可以成为研究、政策、行业和社会之间的机制管道，使食品系统中的所有利益相关者能够及时做出明智的决策并采取行动。食品、营养和健康领域的专业知识应始终融入人工智能驱动的营养智能（NI）的开发和持续应用中，以确保其负责任、准确、安全、可用且有效。饮食行为很复杂，改善与饮食相关的健康状况需要在设计和应用NI工具时考虑社会文化人口因素。本文介绍了食品系统中NI的现有示例和未来机遇。让食品、健康和营养专家参与包括数据采集、处理、输出验证和持续质量保证在内的所有阶段的“人在回路”方法，对于确保循证实践至关重要。该领域应与其他领域一样适用相同的伦理考量（例如隐私、包容性、稳健性、透明度和问责制），负责任的实践必须包括严格的标准并与监管框架保持一致。如今及未来至关重要的是获取合适的高质量食品成分数据集，其中包括反映不断变化的食品格局的市售产品的最新信息，以实现负责任的人工智能帮助应对现有食品系统挑战的潜力。
