Abstract：
OBJECTIVE: Sarcoidosis is a systemic, granulomatous disease of uncertain cause. Diagnosis may be difficult, prognosis uncertain and response to treatment unpredictable. The application of artificial intelligence to sarcoidosis may provide clinical decision support for these challenges. This review will provide an overview of current and potential future applications of artificial intelligence in sarcoidosis.
RESULTS: The predominant application of artificial intelligence in sarcoidosis is imaging. Imaging models may differentiate sarcoidosis from other pulmonary disorders. Models, which predict survival and identify key factors relevant to prognosis are also available. The application of cluster analysis to organize sarcoidosis patients into developmental phenotypes is underway. Machine learning algorithms to evaluate the treatment response of sarcoidosis patients do not yet exist but similar models may evaluate patients with other inflammatory disease. The potential applications of artificial intelligence to sarcoidosis is vast, but there are practical limitations that warrant consideration. These include: the accessibility of data, biases in data, cost and privacy.
CONCLUSIONS: The application of artificial intelligence in medicine is still in its early stages but models are poised to support the diagnostic and prognostic challenges in sarcoidosis patients. The predictive power of these artificial intelligence is likely to come from combining various models, trained on content-rich datasets from phenotypically heterogeneous sarcoidosis patients.
RESULTS: The predominant application of artificial intelligence in sarcoidosis is imaging. Imaging models may differentiate sarcoidosis from other pulmonary disorders. Models, which predict survival and identify key factors relevant to prognosis are also available. The application of cluster analysis to organize sarcoidosis patients into developmental phenotypes is underway. Machine learning algorithms to evaluate the treatment response of sarcoidosis patients do not yet exist but similar models may evaluate patients with other inflammatory disease. The potential applications of artificial intelligence to sarcoidosis is vast, but there are practical limitations that warrant consideration. These include: the accessibility of data, biases in data, cost and privacy.
CONCLUSIONS: The application of artificial intelligence in medicine is still in its early stages but models are poised to support the diagnostic and prognostic challenges in sarcoidosis patients. The predictive power of these artificial intelligence is likely to come from combining various models, trained on content-rich datasets from phenotypically heterogeneous sarcoidosis patients.
摘要：
目的：结节病是一种全身性，原因不明的肉芽肿性疾病。诊断可能很困难,预后不确定，治疗反应不可预测。将人工智能应用于结节病可以为这些挑战提供临床决策支持。这篇综述将概述人工智能在结节病中的当前和潜在的未来应用。
结果：人工智能在结节病中的主要应用是影像学。影像学模型可以将结节病与其他肺部疾病区分开来。模型,预测生存率和确定与预后相关的关键因素也是可用的。应用聚类分析将结节病患者组织成发育表型正在进行中。评估结节病患者治疗反应的机器学习算法尚不存在，但类似的模型可以评估患有其他炎症性疾病的患者。人工智能在结节病中的潜在应用是巨大的，但是有一些实际限制值得考虑。这些包括：数据的可访问性，数据中的偏见，成本和隐私。
结论：人工智能在医学中的应用仍处于早期阶段，但模型已准备好支持结节病患者的诊断和预后挑战。这些人工智能的预测能力很可能来自于各种模型的结合，在表型异质性结节病患者的内容丰富的数据集上进行训练。
结果：人工智能在结节病中的主要应用是影像学。影像学模型可以将结节病与其他肺部疾病区分开来。模型,预测生存率和确定与预后相关的关键因素也是可用的。应用聚类分析将结节病患者组织成发育表型正在进行中。评估结节病患者治疗反应的机器学习算法尚不存在，但类似的模型可以评估患有其他炎症性疾病的患者。人工智能在结节病中的潜在应用是巨大的，但是有一些实际限制值得考虑。这些包括：数据的可访问性，数据中的偏见，成本和隐私。
结论：人工智能在医学中的应用仍处于早期阶段，但模型已准备好支持结节病患者的诊断和预后挑战。这些人工智能的预测能力很可能来自于各种模型的结合，在表型异质性结节病患者的内容丰富的数据集上进行训练。
