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

• 出版年: JMIR Diabetes
• 主要な著者: Elizabeth R Stevens, Arielle Elmaleh-Sachs, Holly Lofton, Devin M Mann
• フォーマット: 論文
• 言語: 英語
• 出版事項: JMIR Publications 2024-11-01
• オンライン･アクセス: https://diabetes.jmir.org/2024/1/e58680

AbstractHighly 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.