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Innovations in Diabetes Management for Pregnant Women: Artificial Intelligence and the Internet of Medical Things

医学 妊娠期糖尿病 远程医疗 健康 心理干预 血糖性 互联网 医疗保健 怀孕 互联网隐私 糖尿病 计算机科学 护理部 万维网 内分泌学 遗传学 妊娠期 经济 生物 经济增长
作者
Ellen M. Murrin,Antonio F. Saad,Scott Sullivan,Yuri Millo,Menachem Miodovnik
出处
期刊:American Journal of Perinatology [Thieme Medical Publishers (Germany)]
标识
DOI:10.1055/a-2489-4462
摘要

Pregnancies impacted by diabetes face the compounded challenge of strict glycemic control with mounting insulin resistance as the pregnancy progresses. New technological advances, including artificial intelligence (AI) and the Internet of Medical Things (IoMT), are revolutionizing healthcare delivery by providing innovative solutions for diabetes care during pregnancy. Together, AI and the IoMT are a multibillion-dollar industry that integrates advanced medical devices and sensors into a connected network that enables continuous monitoring of glucose levels. AI-driven Clinical Decision Support Systems (CDSS) can predict glucose trends and suggest tailor evidenced-based treatments with real-time adjustments as her insulin resistance changes with placental growth. Additionally, mobile health applications (mHealth) facilitate patient education and self-management through real-time tracking of diet, physical activity, and glucose levels. Remote monitoring capabilities are particularly beneficial for pregnant persons with diabetes as they extend quality care to underserved populations and reduce the need for frequent in-person visits. This high-resolution monitoring allows physicians and patients access to an unprecedented wealth of data to make more informed decisions based on real-time data, reducing complications for both the mother and fetus. These technologies can potentially improve maternal and fetal outcomes by enabling timely, individualized interventions based on personalized health data. While AI and IoMT offer significant promise in enhancing diabetes care for improved maternal and fetal outcomes, their implementation must address challenges such as data security, cost-effectiveness, and preserving the essential patient-provider relationship.
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