AI-aided clinical diagnosis is desired in medical care. Existing deep learning models lack explainability and mainly focus on image analysis. The recently developed Dynamic Uncertain Causality Graph (DUCG) approach is causality-driven, explainable, and invariant across different application scenarios, without problems of data collection, labeling, fitting, privacy, bias, generalization, high cost and high energy consumption. Through close collaboration between clinical experts and DUCG technicians, 46 DUCG models covering 54 chief complaints were constructed. Over 1,000 diseases can be diagnosed without triage. Before being applied in real-world, the 46 DUCG models were retrospectively verified by third-party hospitals. The verified diagnostic precisions were no less than 95%, in which the diagnostic precision for every disease including uncommon ones was no less than 80%. After verifications, the 46 DUCG models were applied in the real-world in China. Over one million real diagnosis cases have been performed, with only 17 incorrect diagnoses identified. Due to DUCG's transparency, the mistakes causing the incorrect diagnoses were found and corrected. The diagnostic abilities of the clinicians who applied DUCG frequently were improved significantly. Following the introduction to the earlier presented DUCG methodology, the recommendation algorithm for potential medical checks is presented and the key idea of DUCG is extracted.

翻译：人工智能辅助临床诊断是医疗领域的迫切需求。现有深度学习模型缺乏可解释性，且主要集中于图像分析。新近发展的动态不确定因果图（DUCG）方法具有因果驱动、可解释性强、跨应用场景不变性的特点，避免了数据收集、标注、拟合、隐私、偏见、泛化、高成本及高能耗等问题。通过临床专家与DUCG技术人员的紧密协作，我们构建了覆盖54种主诉的46个DUCG模型。该系统可不经分诊直接诊断超过1000种疾病。在投入真实世界应用前，这46个DUCG模型已由第三方医院进行回顾性验证。经验证的诊断准确率不低于95%，其中每种疾病（包括罕见病）的诊断准确率均不低于80%。验证完成后，46个DUCG模型已在中国投入真实世界应用。系统已执行超过一百万例实际诊断，仅发现17例误诊。得益于DUCG的透明度，导致误诊的错误得以发现并修正。频繁使用DUCG的临床医生的诊断能力得到显著提升。在介绍先前提出的DUCG方法论后，本文进一步提出潜在医学检查的推荐算法，并提炼了DUCG的核心思想。