精准医学中的人工智能:转化疾病亚型、医学成像和药物基因组学。

IF 3.3 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY
Andrea Rodriguez-Martinez, Dilini Kothalawala, Rodrigo M Carrillo-Larco, Antonios Poulakakis-Daktylidis
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引用次数: 0

摘要

精准医疗标志着向以患者为中心的治疗方法的变革,旨在“在正确的时间为正确的患者提供正确的药物”。来自不同组学模式、电子健康记录和医学成像的数据呈指数级增长,为精准医疗创造了前所未有的机会。这种数据爆炸需要先进的处理和分析工具。在这场革命的最前沿是人工智能(AI),它擅长于发现这些高维和复杂数据集中隐藏的模式。人工智能促进了不同数据类型的整合和分析,释放了表征复杂疾病、改善预后和预测治疗反应的无与伦比的潜力。尽管人工智能具有巨大的潜力,但在将这些工具从研究环境转化为临床实践时,出现了与可解释性、可靠性、通用性和伦理考虑相关的挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Artificial intelligence in precision medicine: transforming disease subtyping, medical imaging, and pharmacogenomics.

Artificial intelligence in precision medicine: transforming disease subtyping, medical imaging, and pharmacogenomics.

Artificial intelligence in precision medicine: transforming disease subtyping, medical imaging, and pharmacogenomics.

Artificial intelligence in precision medicine: transforming disease subtyping, medical imaging, and pharmacogenomics.

Precision medicine marks a transformative shift towards a patient-centric treatment approach, aiming to match 'the right patients with the right drugs at the right time'. The exponential growth of data from diverse omics modalities, electronic health records, and medical imaging has created unprecedented opportunities for precision medicine. This explosion of data requires advanced processing and analytical tools. At the forefront of this revolution is artificial intelligence (AI), which excels at uncovering hidden patterns within these high-dimensional and complex datasets. AI facilitates the integration and analysis of diverse data types, unlocking unparalleled potential to characterise complex diseases, improve prognosis, and predict treatment response. Despite the enormous potential of AI, challenges related to interpretability, reliability, generalisability, and ethical considerations emerge when translating these tools from research settings into clinical practice.

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来源期刊
CiteScore
7.70
自引率
0.00%
发文量
94
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