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数字技术与人工智能在医疗保健中的应用:营养评估概述

The Application of Digital Technologies and Artificial Intelligence in Healthcare: An Overview on Nutrition Assessment.

作者信息

Salinari Alessia, Machì Michele, Armas Diaz Yasmany, Cianciosi Danila, Qi Zexiu, Yang Bei, Ferreiro Cotorruelo Maria Soledad, Villar Santos Gracia, Dzul Lopez Luis Alonso, Battino Maurizio, Giampieri Francesca

机构信息

Department of Clinical Sciences, Faculty of Medicine, Polytechnic University of Marche, 60131 Ancona, Italy.

Direzione Medica Ospedaliera, Azienda Ospedaliera-Universitaria delle Marche, 60126 Ancona, Italy.

出版信息

Diseases. 2023 Jul 13;11(3):97. doi: 10.3390/diseases11030097.

DOI:10.3390/diseases11030097
PMID:37489449
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10366918/
Abstract

In the last decade, artificial intelligence (AI) and AI-mediated technologies have undergone rapid evolution in healthcare and medicine, from apps to computer software able to analyze medical images, robotic surgery and advanced data storage system. The main aim of the present commentary is to briefly describe the evolution of AI and its applications in healthcare, particularly in nutrition and clinical biochemistry. Indeed, AI is revealing itself to be an important tool in clinical nutrition by using telematic means to self-monitor various health metrics, including blood glucose levels, body weight, heart rate, fat percentage, blood pressure, activity tracking and calorie intake trackers. In particular, the application of the most common digital technologies used in the field of nutrition as well as the employment of AI in the management of diabetes and obesity, two of the most common nutrition-related pathologies worldwide, will be presented.

摘要

在过去十年中,人工智能(AI)及其介导的技术在医疗保健和医学领域经历了快速发展,从应用程序到能够分析医学图像的计算机软件、机器人手术和先进的数据存储系统。本评论的主要目的是简要描述人工智能的发展及其在医疗保健中的应用,特别是在营养和临床生物化学方面的应用。事实上,人工智能正通过远程信息技术手段自我监测各种健康指标,包括血糖水平、体重、心率、脂肪百分比、血压、活动追踪和卡路里摄入量追踪器,从而成为临床营养领域的一项重要工具。特别是,将介绍营养领域中最常用的数字技术的应用,以及人工智能在糖尿病和肥胖症管理中的应用,这两种疾病是全球最常见的与营养相关的病症。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15a6/10366918/a52073d7bd96/diseases-11-00097-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15a6/10366918/a52073d7bd96/diseases-11-00097-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15a6/10366918/a52073d7bd96/diseases-11-00097-g001.jpg

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2
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Adv Nutr. 2022 Dec 22;13(6):2573-2589. doi: 10.1093/advances/nmac103.
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Personalised Nutrition Approaches in the Prevention and Management of Type 2 Diabetes: A Narrative Review of Evidence and Practice.
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Int J Environ Res Public Health. 2025 Jun 30;22(7):1047. doi: 10.3390/ijerph22071047.
4
The moderating effect of economic development levels on the adoption of technologies in medical education: A multinational survey across six Asian countries.经济发展水平对医学教育技术采用的调节作用:一项对六个亚洲国家的跨国调查。
Digit Health. 2025 Jun 25;11:20552076251350805. doi: 10.1177/20552076251350805. eCollection 2025 Jan-Dec.
5
Multimodal AI in Biomedicine: Pioneering the Future of Biomaterials, Diagnostics, and Personalized Healthcare.生物医学中的多模态人工智能:开创生物材料、诊断和个性化医疗的未来。
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JAMIA Open. 2025 May 28;8(3):ooaf043. doi: 10.1093/jamiaopen/ooaf043. eCollection 2025 Jun.
7
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J Glob Health. 2025 Mar 14;15:04065. doi: 10.7189/jogh.15.04065.
Front Endocrinol (Lausanne). 2021 Sep 6;12:706978. doi: 10.3389/fendo.2021.706978. eCollection 2021.
4
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5
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SN Comput Sci. 2021;2(3):160. doi: 10.1007/s42979-021-00592-x. Epub 2021 Mar 22.
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Machine learning algorithms for predicting malnutrition among under-five children in Bangladesh.孟加拉国五岁以下儿童营养不良预测的机器学习算法。
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