人工智能对生物标志物发现的影响。

IF 3.3 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY
Hira Javaid, Constantin Cezar Petrescu, Lisa J Schmunk, Jack M Monahan, Paul O'Reilly, Manik Garg, Leona McGirr, Mahmoud T Khasawneh, Mustafa Al Lail, Deepak Ganta, Thomas M Stubbs, Benjamin B Sun, Dimitrios Vitsios, Daniel E Martin-Herranz
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引用次数: 0

摘要

人工智能(AI)正在改变许多领域,包括医疗保健和医学。在生物标志物发现方面,人工智能算法产生了深远的影响,因为它们能够从复杂的高维数据集中获得见解,并整合多模态数据类型(如组学、电子健康记录、成像或传感器和可穿戴数据)。然而,尽管人工智能驱动的生物标志物激增,但在将其转化为临床和推动采用方面仍然存在重大障碍,包括缺乏人口多样性、难以获取统一数据、昂贵且耗时的临床研究、不断发展的人工智能监管框架以及缺乏可扩展的诊断基础设施。在这里,我们概述了可用于生物标志物发现的人工智能工具包,并讨论了跨治疗领域人工智能驱动的生物标志物的令人兴奋的例子。最后,我们将解决摆在我们面前的挑战,确保这些技术惠及全球的患者和用户,开启精准医疗快速创新的新时代。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The impact of artificial intelligence on biomarker discovery.

Artificial intelligence (AI) is transforming many fields, including healthcare and medicine. In biomarker discovery, AI algorithms have had a profound impact, thanks to their ability to derive insights from complex high-dimensional datasets and integrate multi-modal datatypes (such as omics, electronic health records, imaging or sensor and wearable data). However, despite the proliferation of AI-powered biomarkers, significant hurdles still remain in translating them to the clinic and driving adoption, including lack of population diversity, difficulties accessing harmonised data, costly and time-consuming clinical studies, evolving AI regulatory frameworks and absence of scalable diagnostic infrastructure. Here, we provide an overview of the AI toolkit available for biomarker discovery, and we discuss exciting examples of AI-powered biomarkers across therapeutic areas. Finally, we address the challenges ahead of us to ensure that these technologies reach patients and users globally and unlock a new era of fast innovation for precision medicine.

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CiteScore
7.70
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