Lightening the Load: Generative AI to Mitigate the Burden of the New Era of Obesity Medical Therapy.

Q2 Medicine
JMIR Diabetes Pub Date : 2024-11-14 DOI:10.2196/58680
Elizabeth R Stevens, Arielle Elmaleh-Sachs, Holly Lofton, Devin M Mann
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引用次数: 0

Abstract

Unlabelled: Highly effective antiobesity and diabetes medications such as glucagon-like peptide 1 (GLP-1) agonists and glucose-dependent insulinotropic polypeptide/GLP-1 (dual) receptor agonists (RAs) have ushered in a new era of treatment of these highly prevalent, morbid conditions that have increased across the globe. However, the rapidly escalating use of GLP-1/dual RA medications is poised to overwhelm an already overburdened health care provider workforce and health care delivery system, stifling its potentially dramatic benefits. Relying on existing systems and resources to address the oncoming rise in GLP-1/dual RA use will be insufficient. Generative artificial intelligence (GenAI) has the potential to offset the clinical and administrative demands associated with the management of patients on these medication types. Early adoption of GenAI to facilitate the management of these GLP-1/dual RAs has the potential to improve health outcomes while decreasing its concomitant workload. Research and development efforts are urgently needed to develop GenAI obesity medication management tools, as well as to ensure their accessibility and use by encouraging their integration into health care delivery systems.

减轻负担:生成式AI减轻肥胖医学治疗新时代的负担。
未标记:高效的抗肥胖和糖尿病药物,如胰高血糖素样肽1 (GLP-1)激动剂和葡萄糖依赖性胰岛素性多肽/GLP-1(双)受体激动剂(RAs),已经开启了治疗这些在全球范围内日益增加的高度流行的病态疾病的新时代。然而,GLP-1/双重类风湿性关节炎药物的使用迅速增加,使已经负担过重的医疗服务人员和医疗服务系统不堪重负,扼杀了其潜在的巨大效益。依靠现有的系统和资源来解决即将到来的GLP-1/双RA使用的增加是不够的。生成式人工智能(GenAI)有可能抵消与这些药物类型的患者管理相关的临床和管理需求。尽早采用GenAI来促进这些GLP-1/双RAs的管理,有可能改善健康结果,同时减少其伴随的工作量。迫切需要开展研究和开发工作,以开发基因肥胖药物管理工具,并通过鼓励将其纳入卫生保健提供系统来确保其可及性和使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
JMIR Diabetes
JMIR Diabetes Computer Science-Computer Science Applications
CiteScore
4.00
自引率
0.00%
发文量
35
审稿时长
16 weeks
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