Methodology and real-world applications of dynamic uncertain causality graph for clinical diagnosis with explainability and invariance

Zhang, Z; Zhang, Q; Jiao, Y; Lu, L; Ma, L; Liu, A; Liu, X; Zhao, J; Xue, Y; Wei, B; Zhang, M; Gao, R; Zhao, H; Lu, J; Li, F; Zhang, Y; Wang, Y; Zhang, L; Tian, F; Hu, J; Gou, X

Methodology and real-world applications of dynamic uncertain causality graph for clinical diagnosis with explainability and invariance

Zhang, Z Zhang, Q Jiao, Y Lu, L Ma, L Liu, A Liu, X Zhao, J Xue, Y Wei, B Zhang, M Gao, R Zhao, H Lu, J

Li, F Zhang, Y Wang, Y Zhang, L Tian, F Hu, J Gou, X

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Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: ARTIFICIAL INTELLIGENCE REVIEW, 2024, 57, (6)
Issue Date:: 2024-05-23

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Field	Value	Language
dc.contributor.author	Zhang, Z
dc.contributor.author	Zhang, Q
dc.contributor.author	Jiao, Y
dc.contributor.author	Lu, L
dc.contributor.author	Ma, L
dc.contributor.author	Liu, A
dc.contributor.author	Liu, X
dc.contributor.author	Zhao, J
dc.contributor.author	Xue, Y
dc.contributor.author	Wei, B
dc.contributor.author	Zhang, M
dc.contributor.author	Gao, R
dc.contributor.author	Zhao, H
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732
dc.contributor.author	Li, F
dc.contributor.author	Zhang, Y
dc.contributor.author	Wang, Y
dc.contributor.author	Zhang, L
dc.contributor.author	Tian, F
dc.contributor.author	Hu, J
dc.contributor.author	Gou, X
dc.date.accessioned	2025-01-21T02:34:31Z
dc.date.available	2025-01-21T02:34:31Z
dc.date.issued	2024-05-23
dc.identifier.citation	ARTIFICIAL INTELLIGENCE REVIEW, 2024, 57, (6)
dc.identifier.issn	0269-2821
dc.identifier.issn	1573-7462
dc.identifier.uri	http://hdl.handle.net/10453/183918
dc.description.abstract	<jats:title>Abstract</jats:title><jats:p>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.</jats:p>
dc.language	English
dc.publisher	SPRINGER
dc.relation.ispartof	ARTIFICIAL INTELLIGENCE REVIEW
dc.relation.isbasedon	10.1007/s10462-024-10763-w
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	46 Information and computing sciences
dc.subject.classification	52 Psychology
dc.title	Methodology and real-world applications of dynamic uncertain causality graph for clinical diagnosis with explainability and invariance
dc.type	Journal Article
utslib.citation.volume	57
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	1702 Cognitive Sciences
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Australian Artificial Intelligence Institute (AAII)
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-01-21T02:34:30Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	57
utslib.citation.issue	6

Abstract:

AbstractAI-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.

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http://hdl.handle.net/10453/183918