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人工智能在肿瘤学中的应用:机遇与挑战。

Artificial intelligence in oncology: chances and pitfalls.

机构信息

Else Kröner Fresenius Zentrum für Digitale Gesundheit and Medizinische Klinik und Poliklinik 1, Universitätsklinikum Carl Gustav Carus, Technische Universität Dresden, Fetscherstr. 74, 01307, Dresden, Germany.

出版信息

J Cancer Res Clin Oncol. 2023 Aug;149(10):7995-7996. doi: 10.1007/s00432-023-04666-6. Epub 2023 Mar 15.

Abstract

Artificial intelligence (AI) has been available in rudimentary forms for many decades. Early AI programs were successful in niche areas such as chess or handwriting recognition. However, AI methods had little practical impact on the practice of medicine until recently. Beginning around 2012, AI has emerged as an increasingly important tool in healthcare, and AI-based devices are now approved for clinical use. These devices are capable of processing image data, making diagnoses, and predicting biomarkers for solid tumors, among other applications. Despite this progress, the development of AI in medicine is still in its early stages, and there have been exponential technical advancements since 2022, with some AI programs now demonstrating human-level understanding of image and text data. In the past, technical advances have led to new medical applications with a delay of a few years. Therefore, now we might be at the beginning of a new era in which AI will become even more important in clinical practice. It is essential that this transformation is humane and evidence based, and physicians must take a leading role in ensuring this, particularly in hematology and oncology.

摘要

人工智能(AI)已经存在了几十年,虽然形式还比较基础。早期的 AI 程序在一些特定领域取得了成功,例如国际象棋或手写识别。然而,直到最近,AI 方法才对医学实践产生了实际影响。大约从 2012 年开始,AI 已成为医疗保健领域越来越重要的工具,基于 AI 的设备现在已获准用于临床使用。这些设备能够处理图像数据、做出诊断和预测实体瘤的生物标志物等。尽管取得了这些进展,但 AI 在医学领域的发展仍处于早期阶段,自 2022 年以来,技术已经取得了指数级的进步,一些 AI 程序现在已经展示出对图像和文本数据的人类水平的理解。过去,技术进步导致新的医疗应用出现,通常会有几年的延迟。因此,现在我们可能正处于一个新时代的开端,AI 在临床实践中将变得更加重要。至关重要的是,这种转变应该是人性化和基于证据的,医生必须在确保这一点方面发挥主导作用,尤其是在血液学和肿瘤学领域。

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Artificial intelligence in oncology: chances and pitfalls.人工智能在肿瘤学中的应用:机遇与挑战。
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