数字治疗对人工智能的需求。

Q1 Computer Science Digital Biomarkers Pub Date : 2020-04-08 eCollection Date: 2020-01-01 DOI:10.1159/000506861
Adam Palanica, Michael J Docktor, Michael Lieberman, Yan Fossat
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引用次数: 26

摘要

数字治疗是医疗保健领域的一个新概念,它提出了使用各种数字技术改变患者行为和治疗医疗条件的概念。然而,很少有标准来定义该术语,使其与传统治疗的简单数字化版本区分开来。我们的目标是描述数字疗法的一个更有价值的特征,它与传统医学或疗法不同:即利用人工智能和机器学习系统,通过数字生物标志物在自适应临床反馈回路中监测和预测个体患者症状数据,为医疗保健提供精准医学方法。人工智能平台可以使用大量的个人变量来学习和预测有效的干预措施,从而提供定制化的治疗方案。数字疗法与人工智能和机器学习相结合,还可以在人群水平上对各种健康状况和群体进行更有效的临床观察和管理。与其他形式的治疗相比,数字治疗的这一重要区别使医疗保健更加个性化,能够积极适应患者的个人临床需求、目标和生活方式。重要的是,为了推进整个数字医疗领域,这些特征是需要向患者、医生和政策制定者强调的。
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The Need for Artificial Intelligence in Digital Therapeutics.

Digital therapeutics is a newly described concept in healthcare which is proposed to change patient behavior and treat medical conditions using a variety of digital technologies. However, the term is rarely defined with criteria that make it distinct from simply digitizedversions of traditional therapeutics. Our objective is to describe a more valuable characteristic of digital therapeutics, which is distinct from traditional medicine or therapy: that is, the utilization of artificial intelligence and machine learning systems to monitor and predict individual patient symptom data in an adaptive clinical feedback loop via digital biomarkers to provide a precision medicine approach to healthcare. Artificial intelligence platforms can learn and predict effective interventions for individuals using a multitude of personal variables to provide a customized and more tailored therapy regimen. Digital therapeutics coupled with artificial intelligence and machine learning also allows more effective clinical observations and management at the population level for various health conditions and cohorts. This vital differentiation of digital therapeutics compared to other forms of therapeutics enables a more personalized form of healthcare that actively adapts to patients' individual clinical needs, goals, and lifestyles. Importantly, these characteristics are what needs to be emphasized to patients, physicians, and policy makers to advance the entire field of digital healthcare.

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来源期刊
Digital Biomarkers
Digital Biomarkers Medicine-Medicine (miscellaneous)
CiteScore
10.60
自引率
0.00%
发文量
12
审稿时长
23 weeks
期刊最新文献
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