Detecting and Mitigating the Clever Hans Effect in Medical Imaging: A Scoping Review.
The Clever Hans effect occurs when machine learning models rely on spurious correlations instead of clinically relevant features and poses significant challenges to the development of reliable artificial intelligence (AI) systems in medical imaging. This scoping review provides an overview of method...
| Published in: | Journal of Imaging Informatics in Medicine Vol. 38; no. 4; pp. 2563 - 2580 |
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| Main Authors: | , , , , , , , , , , , , |
| Format: | research systematic review tables/charts Journal Article |
| Published: |
Springer Nature
Aug2025
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187278972&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187278972 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Aug2025 vid: 38 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 187278972 187278972 187278972 10.1007/s10278-024-01335-z 187278972 ppf: 2563 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Detecting and Mitigating the Clever Hans Effect in Medical Imaging: A Scoping Review. aug: au: Vásquez-Venegas, Constanza Wu, Chenwei Sundar, Saketh Prôa, Renata Beloy, Francis Joshua Medina, Jillian Reeze McNichol, Megan Parvataneni, Krishnaveni Kurtzman, Nicholas Mirshawka, Felipe Aguirre-Jerez, Marcela Ebner, Daniel K. Celi, Leo Anthony affil: https://ror.org/047gc3g35 Scientific Image Analysis Lab, Faculty of Medicine, Universidad de Chile, 8380453, Santiago, RM, Chile sug: subj: Diagnostic Imaging Methods Artificial Intelligence Detection Algorithms Prediction Models Machine Learning Image Interpretation, Computer Assisted Scoping Review PubMed Embase Gray Literature Thematic Analysis Radiography, Thoracic Brain Radiography Magnetic Resonance Imaging Multidisciplinary Care Team Collaboration Funding Source ab: The Clever Hans effect occurs when machine learning models rely on spurious correlations instead of clinically relevant features and poses significant challenges to the development of reliable artificial intelligence (AI) systems in medical imaging. This scoping review provides an overview of methods for identifying and addressing the Clever Hans effect in medical imaging AI algorithms. A total of 173 papers published between 2010 and 2024 were reviewed, and 37 articles were selected for detailed analysis, with classification into two categories: detection and mitigation approaches. Detection methods include model-centric, data-centric, and uncertainty and bias-based approaches, while mitigation strategies encompass data manipulation techniques, feature disentanglement and suppression, and domain knowledge-driven approaches. Despite the progress in detecting and mitigating the Clever Hans effect, the majority of current machine learning studies in medical imaging do not report or test for shortcut learning, highlighting the need for more rigorous validation and transparency in AI research. Future research should focus on creating standardized benchmarks, developing automated detection tools, and exploring the integration of detection and mitigation strategies to comprehensively address shortcut learning. Establishing community-driven best practices and leveraging interdisciplinary collaboration will be crucial for ensuring more reliable, generalizable, and equitable AI systems in healthcare. pubtype: Academic Journal doctype: research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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