Artificial intelligence-guided precision medicine in hematological disorders
Artificial intelligence-guided precision medicine in hematological disorders
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人工智能引导血液疾病精准医疗
DOI：
10.11406/rinketsu.61.554
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
横山 和明
中科院分区：
文献类型：
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作者：
大河原冬彩;林亜佳音;小林幸司;永田奈々恵;村田幸久;横山 和明
肿瘤学中的精准医学使用基因组数据在正确的时间为正确的患者提供正确的干预。为此，下一代测序（NGS）是不可或缺的工具。然而，进一步的创新是必要的，包括使用体细胞突变来告知临床干预的医学信息学。这个过程被称为人工解释或策展，目前是劳动密集型的，涉及经验丰富的策展人，他们在不断增长的知识体系中识别相关证据，并将其转化为医疗实践。为了解决这个问题，自2015年以来，我们一直在组织临床测序（CS）团队，该团队在人工智能（AI）的帮助下整合与血液恶性肿瘤相关的临床和基因组信息。到目前为止，我们已经整理了300多名血液癌症患者的AI辅助CS数据。在本文中，我们为非专业人士提供了一个简单的介绍，手动解释过程，以及一个代表性的人工智能平台，沃森基因组学的概述。根据我们自己的经验，我们强调了为什么在CS的人工解释过程中需要AI。我们还提出了一些AI的陷阱和局限性，血液学家在解释AI输出时应该意识到这些缺陷和局限性。
Precision medicine in oncology uses genomic data to provide the right intervention in the right patients at the right time. For this purpose, next-generation sequencing (NGS) is an indispensable tool. However, further innovations are necessary, including medical informatics which uses somatic mutations to inform clinical intervention. This process, called manual interpretation or curation, is currently labor-intensive, involving experienced curators who identify the relevant evidence among a growing body of knowledge and translate it into medical practice. To address this issue, since 2015, we have been organizing a clinical sequencing (CS) team, which integrates clinical and genomic information related to hematological malignancies with the aid of artificial intelligence (AI). So far, we have collated AI-assisted CS data for more than 300 patients with hematological cancers. In this paper, we provide a brief introduction for the nonspecialist to the manual interpretation process together with an overview of a representative AI platform, Watson for Genomics. Based on our own experience, we highlight why AI is needed in the manual interpretation process in CS. We also present some of the pitfalls and limitations of AI that hematologists should be aware of when interpreting AI-output.