National Engineering Research Center of Deep Processing of Rice and Byproducts, College of Food Science and Engineering, Central South University of Forestry and Technology, Changsha, 410004, PR China.
National Engineering Research Center of Deep Processing of Rice and Byproducts, College of Food Science and Engineering, Central South University of Forestry and Technology, Changsha, 410004, PR China; SINOCARE Inc., Changsha, 410004, PR China.
Comput Biol Med. 2024 Aug;178:108711. doi: 10.1016/j.compbiomed.2024.108711. Epub 2024 Jun 4.
With the rapid development of information technology and artificial intelligence (AI), people have acquired the abilities and are encouraged to develop intelligent tools and software, which begins to shed light on intelligent and precise food nutrition. Despite the rapid development of such software, disparities still exist in terms of methodology, contents, and implementation strategies. Hence, a set of panoramic profiles is urgently needed to elucidate their values and guide their future development. Here a comprehensive review was conducted aiming to summarize and compare the objects, contents, intelligent algorithms, and functions realized by the already released software in current research. Consequently, 177 AI nutritionists in recent years were collected and analyzed. The advantages, limitations, and trends concerning their application scenarios were analyzed. It was found that AI nutritionists have been gradually advancing the production modes and efficiency of food recognition, dietary recording/monitoring, nutritional assessment, and nutrient/recipe recommendation. Most AI nutritionists have a relatively low level of intelligence. However, new trends combining advanced AI algorithms, intelligent sensors and big data are coming with new applications in real-time and precision nutrition. AI models concerning molecular-level behaviors are becoming the new focus to drive AI nutritionists. Multi-center and multi-level studies have also gradually been realized to be necessary.
随着信息技术和人工智能（AI）的飞速发展，人们已经具备了开发智能工具和软件的能力，并受到鼓励，这开始为智能和精准的食品营养带来了曙光。尽管这类软件发展迅速，但在方法、内容和实施策略方面仍存在差异。因此，迫切需要一套全景式的简介来阐明其价值并指导其未来发展。在这里，我们进行了一项全面的综述，旨在总结和比较当前研究中已发布软件的对象、内容、智能算法和实现的功能。因此，我们收集和分析了近年来的 177 位人工智能营养学家。分析了它们应用场景的优缺点和趋势。结果发现，人工智能营养学家正在逐渐推进食品识别、饮食记录/监测、营养评估和营养/配方推荐的生产模式和效率。大多数人工智能营养学家的智能水平相对较低。然而，结合先进的 AI 算法、智能传感器和大数据的新趋势正在为实时和精准营养带来新的应用。涉及分子级行为的 AI 模型正成为推动人工智能营养学家发展的新焦点。多中心和多层次的研究也逐渐被认为是必要的。
