Doctor vs. AI 医生和人工智能

　　Medical science stands at the beginning of a digital revolution, that will fundamentally change its shape in the coming years and decades. Technologies that seemed merely science fiction are already a reality today. While terms like Medicine 2.0 and Big Data are still discussed among experts, some countries have already made connected, digital solutions, as well as tele- and internet medicine, part of their regular health care provision. Still, the advancement of artificial intelligence might soon overshadow all other future developments.

　　医学正处于一场数字革命的开端，这将在未来几年和几十年里从根本上改变其形态。似乎仅仅是科幻小说的技术在今天已经成为现实。虽然医学2.0和大数据等术语仍在专家中讨论，但一些国家已经将联网的数字解决方案以及远程和互联网医学作为其常规医疗服务的一部分。尽管如此，人工智能的进步可能很快会掩盖所有其他未来的发展。

　　In the last decades, hardly any other discipline has been as strongly characterized by technical progress as radiology. There is an incredible jump between the development of the first x-ray films and the introduction of computer-tomography (CT) and Magnetic resonance imaging (MRI). The improvements in medical imaging have made diagnostics not possible, but also easier in many areas. At the same time, the amount of data recorded increased, together with the development of tomographers tremendously. Already today, data analysis is impossible without the proper software. At the same time the work of the radiologist has changed from pattern recognition to the proverbial search of ‘a needle in a haystack’.

　　在过去的几十年里，几乎没有任何其他学科像放射学一样具有技术进步的强烈特征。在第一批x射线胶片的发展和计算机断层扫描( CT )和磁共振成像( MRI )的引入之间有一个不可思议的飞跃。医学成像的改进使得诊断成为可能，但在许多领域也变得更加容易。与此同时，随着断层摄影技术的发展，记录的数据量增加了。如今，没有合适的软件，数据分析已经是不可能的了。与此同时，放射科医生的工作已经从模式识别转变为谚语中的“大海捞针”。

　　Computers are already able to take over parts of the radiological diagnostics and are not only faster but often also more accurate than humans. Moreover, they do not tire and as a result and the quality of the analysis remains consistent. While computers can perform an immense amount of complicated computing operations in a fraction of a second, they are still inferior to humans in other tasks, such as the differentiation of faces and objects. With the advancement of artificial intelligence, computers have stopped following rigidly programmed rules. The so-called "deep learning" is considered a breakthrough within the field of machine learning. This technology makes use of artificial neural networks and imitates the operating mode of the human brain. A special feature of this technology is that the quality of the analytical results increases with the amount of processed data. For all intents and purposes, deep learning is comparable to a person who learns and gains in experience.

　　计算机已经能够接管放射诊断的部分工作，不仅比人类更快，而且往往更精确。此外，它们不会疲劳，因此分析的质量保持一致。虽然计算机可以在几分之一秒内完成大量复杂的计算操作，但在其他任务中，比如人脸和物体的区分，它们仍然不如人类。随着人工智能的发展，计算机已经不再遵循严格编程的规则。所谓的“深度学习”被认为是机器学习领域的一个突破。这项技术利用人工神经网络，模拟人脑的运作模式。这项技术的一个特点是，分析结果的质量随着处理数据量的增加而提高。就所有意图和目的而言，深度学习可与从经验中学习和获得的人相提并论。

　　Nowadays, deep learning is also used in medicine. The US-American Startup "Enlitic" uses this technology to evaluate x-ray images, for instance to recognize fractures as well as lung tumors independently. IBM's computer system "Watson" is based on this technology as well and attracted a lot of media coverage in 2011 when "Watson" won against humans in the American quiz show "Jeopardy!". However, the system is universally applicable and also capable of evaluating CT images and recognizing anomalies such as dissections and pulmonary embolisms.

　　如今，深度学习也被用于医学。美国创业公司“Enlitic”使用这项技术评估x射线图像，例如独立识别骨折和肺部肿瘤。IBM的计算机系统“沃森”也是基于这项技术，2011年，当“沃森”在美国智力竞赛节目“危险”中战胜人类时，吸引了大量媒体报道。"。然而，该系统普遍适用，并且能够评估CT图像并识别异常，如解剖和肺栓塞。

　　In the coming years, artificial intelligence will play a growing role in many areas of professional and private life, and especially in medicine. Radiology is one of the first disciplines in which human and artificial intelligence will closely work together. While this technology might not replace radiologists in the foreseeable future, it certainly will fundamentally alter their work. Instead of diagnosing dozens of images every day, a radiologist might instead be able to access already processed data and place it within its clinical context more and more. Even if computers should be superior to humans in analytical speed and quality, ultimately humans will still decide whether a new physical development is of any pathological significance or relevance.

　　未来几年，人工智能将在职业和私人生活的许多领域，特别是医学领域发挥越来越大的作用。放射学是人类和人工智能紧密合作的首批学科之一。虽然这项技术在可预见的将来可能不会取代放射学家，但它肯定会从根本上改变他们的工作。不是每天诊断几十幅图像，放射科医生可能反而能够访问已经处理过的数据，并越来越多地将其放入临床环境中。即使计算机在分析速度和质量上优于人类，最终人类仍将决定新的物理发展是否具有病理意义或相关性。