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.

基于动态不确定因果图 (DUCG) 的人工智能辅助临床诊断，具有可解释性、高诊断精度和推荐算法。