Deep learning has emerged as the leading method in machine learning, spawning a rapidly growing field of academic research and commercial applications across medicine. Deep learning could have particular relevance to rheumatology if correctly utilized. The greatest benefits of deep learning methods are seen with unstructured data frequently found in rheumatology, such as images and text, where traditional machine learning methods have struggled to unlock the trove of information held within these data formats. The basis for this success comes from the ability of deep learning to learn the structure of the underlying data. It is no surprise that the first areas of medicine that have started to experience impact from deep learning heavily rely on interpreting visual data, such as triaging radiology workflows and computer-assisted colonoscopy. Applications in rheumatology are beginning to emerge, with recent successes in areas as diverse as detecting joint erosions on plain radiography, predicting future rheumatoid arthritis disease activity, and identifying halo sign on temporal artery ultrasound. Given the important role deep learning methods are likely to play in the future of rheumatology, it is imperative that rheumatologists understand the methods and assumptions that underlie the deep learning algorithms in widespread use today, their limitations and the landscape of deep learning research that will inform algorithm development, and clinical decision support tools of the future. The best applications of deep learning in rheumatology must be informed by the clinical experience of rheumatologists, so that algorithms can be developed to tackle the most relevant clinical problems.

中文翻译：

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面向风湿病学家的人工智能和深度学习

深度学习已成为机器学习的主要方法，催生了一个快速发展的跨医学学术研究和商业应用领域。如果使用得当，深度学习可能与风湿病学特别相关。深度学习方法的最大好处体现在风湿病学中经常发现的非结构化数据，例如图像和文本，传统的机器学习方法一直在努力解锁这些数据格式中包含的信息宝库。这种成功的基础来自于深度学习学习底层数据结构的能力。毫不奇怪，第一个开始受到深度学习影响的医学领域严重依赖于解释视觉数据，例如分类放射学工作流程和计算机辅助结肠镜检查。风湿病学的应用开始出现，最近在平片检测关节侵蚀、预测未来类风湿性关节炎疾病活动以及识别颞动脉超声晕征等领域取得了成功。鉴于深度学习方法可能在风湿病学的未来发挥重要作用，风湿病学家必须了解当今广泛使用的深度学习算法背后的方法和假设、它们的局限性和深度学习研究的前景算法开发和未来的临床决策支持工具。深度学习在风湿病学中的最佳应用必须借鉴风湿病学家的临床经验，以便开发算法来解决最相关的临床问题。