Causability and explainability of artificial intelligence in medicine
Open accessAbstract
Explainable artificial intelligence (AI) is attracting much interest in medicine. Technically, the problem of explainability is as old as AI itself and classic AI represented comprehensible retraceable approaches. However, their weakness was in dealing with uncertainties of the real world. Through the introduction of probabilistic learning, applications became increasingly successful, but increasingly opaque. Explainable AI deals with the implementation of transparency and traceability of statistical black‐box machine learning methods, particularly deep learning (DL). We argue that there is a need to go beyond explainable AI. To reach a level of explainable medicine we need causability. In the same way that usability encompasses measurements for the quality of use, causability encompasses measurements for the quality of explanations. In this article, we provide some necessary definitions to discriminate between explainability and causability as well as a use‐case of DL interpretation and of human explanation in histopathology. The main contribution of this article is the notion of causability, which is differentiated from explainability in that causability is a property of a person, while explainability is a property of a system This article is categorized under: Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction
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Traditional machine learning approaches often struggle to accommodate the vagueness and semantic variability inherent in mental health records.
“Traditional machine learning approaches often struggle to accommodate the vagueness and semantic variability inherent in mental health records (Holzinger et al., 2019).”
From A hybrid fuzzy logic–Random Forest model to predict psychiatric treatment order outcomes: an interpretable tool for legal decision support (2025) · cites this paper as Holzinger et al., 2019
The Introduction and Motivation (page unavailable) says medical AI faces uncertain and heterogeneous data, supporting the general point that medical data pose challenges for machine learning. It does not specifically discuss mental health records, their vagueness or semantic variability, or traditional approaches’ ability to handle those features. (LLM, conf=0.82)
Experimental — accuracy not yet measuredAI and DL techniques can be used to process data from sensors such as cameras, lidar, and radar to identify and track objects in real-time for autonomous vehicles.
“AI and DL techniques can be used to process data from sensors such as cameras, lidar, and radar to identify and track objects in real-time [126] .”
From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [126]
The Conclusion (page not supplied) says that deep-learning algorithms work well in autonomous driving, and the section on interpreting deep neural networks says they can perform image and movement recognition. Neither passage describes processing camera, lidar, or radar data to identify and track objects in real time. (LLM, conf=0.84)
Experimental — accuracy not yet measuredAI systems should support the understanding and explanation of the causal models as opposed to simply solving through pattern recognition.
“In health care, causality is especially important when using automated decision systems; therefore, Holzinger et al [33] emphasized that AI systems should support the understanding and explanation of the causal models as opposed to simply solving through pattern recognition.”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [33]
There is a recognized need to go beyond causality/correlation to true "causability".
“Additionally, XAI can help to shed some light onto causality [18,46,67] although, there is a recognized need to go beyond causality/correlation to true “causability” [3].”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [3]
Explainability of AI tools will enhance the trust of medical professionals.
“This also has important implications for the public, policy, and governance as the explainability of AI tools will enhance the trust of medical professionals [3].”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [3]
There is often a perceived trade-off between the performance (predictive accuracy) of a model and explainability.
“There is often a perceived trade-off between the performance (predictive accuracy) of a model and explainability [3,18,19].”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [3]
In some scenarios, explainability of AI output will be a requirement for the output to be used at all, in particular in high-stakes or high-pressure scenarios.
“In some scenarios, explainability of AI output will be a requirement for the output to be used at all, in particular in high-stakes or high-pressure scenarios [3].”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [3]
Lack of interpretability and explainability in ML and DL models can lead to ethical issues and a loss of trust in healthcare.
“In the healthcare context, the lack of interpretability [10] and explainability [11] in ML and DL models can lead to ethical issues and a loss of trust [12], [13].”
From Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond (2024) · cites this paper as [13]
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Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892 · see citing works
Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568 · see citing works
Artificial intelligence: a modern approach
Kumar, D. A., Balasubramanian, S., Byeon, D. H., & Manerkar, P. G. V. (1995) · Choice Reviews Online · doi:10.5860/choice.33-1577 · Cited by 22,182 · see citing works
GAN(Generative Adversarial Nets)
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 19,268 · see citing works
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570 · see citing works
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555 · see citing works
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362 · see citing works
Advances in Neural Information Processing Systems 29
Teymur, O., Zygalakis, K., & Calderhead, B. (2016) · Cited by 13,881 · see citing works
Representation Learning: A Review and New Perspectives
Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001 · see citing works
Causality: Models, Reasoning, and Inference
Pearl, J. (2009) · doi:10.1017/cbo9780511803161 · Cited by 11,653 · see citing works
Learning Deep Features for Discriminative Localization
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.319 · Cited by 11,428 · see citing works
Machine learning: Trends, perspectives, and prospects
Jordan, M. I., & Mitchell, T. M. (2015) · Science · doi:10.1126/science.aaa8415 · Cited by 9,955 · see citing works
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016) · arXiv · doi:10.48550/arxiv.1603.04467 · Cited by 9,408 · see citing works
Learning Deep Architectures for AI
Bengio, Y. (2009) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000006 · Cited by 7,048 · see citing works
The mythos of model interpretability
Lipton, Z. C. (2018) · Communications of the ACM · doi:10.1145/3233231 · Cited by 5,427 · see citing works
Explanation in artificial intelligence: Insights from the social sciences
Miller, T. (2019) · Artificial Intelligence · doi:10.1016/j.artint.2018.07.007 · Cited by 5,302 · see citing works
Learning Deep Architectures for AI
Bengio, Y. (2009) · now publishers, Inc. eBooks · doi:10.1561/9781601982957 · Cited by 5,037 · see citing works
On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., & Samek, W. (2015) · PLOS ONE · doi:10.1371/journal.pone.0130140 · Cited by 4,524 · see citing works
Axiomatic attribution for deep networks
Sundararajan, M., Taly, A., & Yan, Q. (2017) · arXiv · doi:10.48550/arxiv.1703.01365 · Cited by 4,087 · see citing works
Pixel Recurrent Neural NetworksPreprint
van den Oord, A., Kalchbrenner, N., & Kavukcuoglu, K. (2016) · International Conference on Machine Learning · doi:10.48550/arxiv.1601.06759 · Cited by 3,111 · see citing works
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
Kendall, A., & Gal, Y. (2017) · arXiv · doi:10.48550/arxiv.1703.04977 · Cited by 3,088 · see citing works
A Practical Guide to Training Restricted Boltzmann Machines
Hinton, G. E. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-35289-8_32 · Cited by 2,852 · see citing works
Methods for interpreting and understanding deep neural networks
Montavon, G., Samek, W., & Müller, K.-R. (2018) · Digital Signal Processing · doi:10.1016/j.dsp.2017.10.011 · Cited by 2,665 · see citing works
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
Schlegl, T., Seeböck, P., Waldstein, S. M., Schmidt-Erfurth, U., & Langs, G. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-59050-9_12 · Cited by 2,627 · see citing works
Self-citation(2013) · Springer eBooks · Cited by 2,625 · see citing works
Building machines that learn and think like people
Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017) · Behavioral and Brain Sciences · doi:10.1017/s0140525x16001837 · Cited by 2,550 · see citing works
Advances in Neural Information Processing Systems 27 (NIPS 2014)
Zhong, M., Goddard, N., & Sutton, C. A. (2014) · Cited by 2,440 · see citing works
Lee, H., Grosse, R., Ranganath, R., & Ng, A. Y. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553453 · Cited by 2,412 · see citing works
Ting, D. S. W., Cheung, C. Y.-L., Lim, G., Tan, G. S. W., Quang, N. D., Gan, A., Hamzah, H., Garcia-Franco, R., San Yeo, I. Y., Lee, S. Y., et al. (2017) · JAMA · doi:10.1001/jama.2017.18152 · Cited by 2,402 · see citing works
30 years of adaptive neural networks: perceptron, Madaline, and backpropagation
Widrow, B. C., & Lehr, M. A. (1990) · Proceedings of the IEEE · doi:10.1109/5.58323 · Cited by 2,241 · see citing works
The Path to Personalized Medicine
Hamburg, M. A., & Collins, F. S. (2010) · New England Journal of Medicine · doi:10.1056/nejmp1006304 · Cited by 1,970 · see citing works
How to grow a mind: Statistics, structure, and abstraction
Tenenbaum, J. B., Kemp, C., Griffiths, T. L., & Goodman, N. D. (2011) · Science · doi:10.1126/science.1192788 · Cited by 1,815 · see citing works
Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission
Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., & Elhadad, N. (2015) · Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2783258.2788613 · Cited by 1,685 · see citing works
A model of inexact reasoning in medicine
Shortliffe, E. H., & Buchanan, B. G. (1975) · Mathematical Biosciences · doi:10.1016/0025-5564(75)90047-4 · Cited by 1,447 · see citing works
Deep supervised, but not unsupervised, models may explain IT cortical representation
Khaligh-Razavi, S.-M., & Kriegeskorte, N. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003915 · Cited by 1,204 · see citing works
Classification of breast cancer histology images using Convolutional Neural Networks
Araújo, T., Aresta, G., Castro, E., Rouco, J., Aguiar, P., Eloy, C., Polónia, A., & Campilho, A. (2017) · PLoS ONE · doi:10.1371/journal.pone.0177544 · Cited by 1,017 · see citing works
Machine learning: An artificial intelligence approach
Stefik, M. (1985) · Artificial Intelligence · doi:10.1016/0004-3702(85)90005-0 · Cited by 982 · see citing works
Machine Learning Methods for Histopathological Image Analysis
Komura, D., & Ishikawa, S. (2018) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2018.01.001 · Cited by 937 · see citing works
Interactive machine learning for health informatics: when do we need the human-in-the-loop?
Holzinger, A. (2016) · Brain Informatics · doi:10.1007/s40708-016-0042-6 · Cited by 906 · see citing works
Self-citationUsability engineering methods for software developers
Holzinger, A. (2005) · Communications of the ACM · doi:10.1145/1039539.1039541 · Cited by 859 · see citing works
Self-citationElements of Causal Inference: Foundations and Learning Algorithms
Peters, J., Janzing, D., & Schölkopf, B. (2017) · OAPEN (The OAPEN Foundation) · Cited by 794 · see citing works
Designing fuzzy inference systems from data: An interpretability-oriented review
Serge Guillaume, A. (2001) · IEEE Transactions on Fuzzy Systems · doi:10.1109/91.928739 · Cited by 754 · see citing works
Computational intelligence: a logical approach
(1998) · Choice Reviews Online · doi:10.5860/choice.35-5701 · Cited by 726 · see citing works
Model-Agnostic Interpretability of Machine Learning
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · arXiv · doi:10.48550/arxiv.1606.05386 · Cited by 699 · see citing works
Computational rationality: A converging paradigm for intelligence in brains, minds, and machines
Gershman, S. J., Horvitz, E., & Tenenbaum, J. B. (2015) · Science · doi:10.1126/science.aac6076 · Cited by 677 · see citing works
What do we need to build explainable AI systems for the medical domain?
Holzinger, A., Biemann, C., Pattichis, C. S., & Kell, D. B. (2017) · arXiv · doi:10.48550/arxiv.1712.09923 · Cited by 636 · see citing works
Self-citationMetrics for explainable AI: challenges and prospects
Hoffman, R. R., Mueller, S. T., Klein, G., & Litman, J. (2018) · arXiv · doi:10.48550/arxiv.1812.04608 · Cited by 518 · see citing works
Measuring usability as quality of use
Bevan, N. (1995) · Software Quality Journal · doi:10.1007/bf00402715 · Cited by 457 · see citing works
Synthesizing the preferred inputs for neurons in neural networks via deep generator networksPreprint
Nguyen, A., Dosovitskiy, A., Yosinski, J., Brox, T., & Clune, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.09304 · Cited by 435 · see citing works
Deep Learning is Robust to Massive Label Noise
Rolnick, D., Veit, A., Belongie, S., & Shavit, N. (2017) · arXiv · doi:10.48550/arxiv.1705.10694 · Cited by 402 · see citing works
Elements of causal inference: foundations and learning algorithms
Shanmugam, R. (2018) · Journal of Statistical Computation and Simulation · doi:10.1080/00949655.2018.1505197 · Cited by 392 · see citing works
NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE
Hilton, W. (1983) · Cited by 376 · see citing works
Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution
Pearl, J. (2018) · Web Search and Data Mining · doi:10.1145/3159652.3176182 · Cited by 375 · see citing works
What Does Explainable AI Really Mean? A New Conceptualization of Perspectives
Doran, D., Schulz, S., & Besold, T. R. (2017) · arXiv · doi:10.48550/arxiv.1710.00794 · Cited by 333 · see citing works
Chess-Playing Programs and the Problem of Complexity
Newell, A., Shaw, J. C., & Simon, H. A. (1958) · IBM Journal of Research and Development · doi:10.1147/rd.24.0320 · Cited by 321 · see citing works
From Machine Learning to Explainable AI
Holzinger, A. (2018) · 2018 World Symposium on Digital Intelligence for Systems and Machines (DISA) · doi:10.1109/disa.2018.8490530 · Cited by 306 · see citing works
Self-citationWeakly supervised histopathology cancer image segmentation and classification
Xu, Y. L., Zhu, J.-Y., Chang, E., Lai, M., & Tu, Z. (2014) · Medical Image Analysis · doi:10.1016/j.media.2014.01.010 · Cited by 280 · see citing works
Goebel, R., Chander, A., Holzinger, K., Lecue, F., Akata, Z., Stumpf, S., Kieseberg, P., & Holzinger, A. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-99740-7_21 · Cited by 259 · see citing works
Self-citationA review of explanation methods for Bayesian networks
Lacave, C., & Díez, F. J. (2002) · The Knowledge Engineering Review · doi:10.1017/s026988890200019x · Cited by 240 · see citing works
Interactive machine learning: experimental evidence for the human in the algorithmic loop
Holzinger, A., Plass, M., Kickmeier-Rust, M., Holzinger, K., Crişan, G. C., Pintea, C.-M., & Palade, V. (2018) · Applied Intelligence · doi:10.1007/s10489-018-1361-5 · Cited by 231 · see citing works
Self-citationHolzinger, A., Dehmer, M., & Jurišica, I. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-s6-i1 · Cited by 219 · see citing works
Self-citationMiller, T., Howe, P., & Sonenberg, L. (2017) · arXiv · doi:10.48550/arxiv.1712.00547 · Cited by 218 · see citing works
Explanations in knowledge systems: design for explainable expert systems
Swartout, W., Paris, C. L., & Moore, J. D. (1991) · IEEE Expert · doi:10.1109/64.87686 · Cited by 200 · see citing works
Human Activity Recognition Using Recurrent Neural Networks
Singh, D., Merdivan, E., Psychoula, I., Kropf, J., Hanke, S., Geist, M., & Holzinger, A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66808-6_18 · Cited by 194 · see citing works
Self-citationGuide to Latin in International Law
Fellmeth, A. X., & Horwitz, M. (2009) · Oxford University Press eBooks · doi:10.1093/acref/9780195369380.001.0001 · Cited by 162 · see citing works
Visual Feature Attribution Using Wasserstein GANs
Baumgartner, C. F., Koch, L. M., Tezcan, K. C., Ang, J. X., & Konukoğlu, E. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00867 · Cited by 151 · see citing works
Interpretable and Explorable Approximations of Black Box ModelsPreprint
Lakkaraju, H., Kamar, E. S., Caruana, R., & Leskovec, J. (2017) · arXiv (Cornell University) · Cited by 123 · see citing works
Unsupervised Identification of Disease Marker Candidates in Retinal OCT Imaging Data
Seeböck, P., Waldstein, S. M., Riedl, S., Bogunović, H., Schlegl, T., Gerendas, B. S., Donner, R., Schmidt-Erfurth, U. M., & Langs, G. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2877080 · Cited by 112 · see citing works
Self-citationVisual explanation of evidence in additive classifiers
Poulin, B., Eisner, R., Szafron, D., Lu, P., Greiner, R., Wishart, D. S., Fyshe, A., Pearcy, B., Macdonell, C., & Anvik, J. (2006) · Cited by 100 · see citing works
Szafron, D., Lu, P., Greiner, R., Wishart, D. S., Poulin, B., Eisner, R., Lu, Z., Anvik, J., Macdonell, C., Fyshe, A., et al. (2004) · Nucleic Acids Research · doi:10.1093/nar/gkh485 · Cited by 100 · see citing works
Bologna, G., & Hayashi, Y. (2017) · Journal of Artificial Intelligence and Soft Computing Research · doi:10.1515/jaiscr-2017-0019 · Cited by 99 · see citing works
Agents that Learn to Explain Themselves
Johnson, W. L. (1994) · Cited by 93 · see citing works
Holzinger, A., Plass, M., Holzinger, K., Crişan, G. C., Pintea, C.-M., & Palade, V. (2019) · Creative Mathematics and Informatics · doi:10.37193/cmi.2019.02.04 · Cited by 86 · see citing works
Self-citationBiffi, C., Oktay, O., Tarroni, G., Bai, W., de Marvao, A., Doumou, G., Rajchl, M., Bedair, R., Prasad, S. K., Cook, S. A., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00934-2_52 · Cited by 80 · see citing works
Holzinger, A. (2014) · Cited by 66 · see citing works
Self-citationIntroduction to MAchine Learning & Knowledge Extraction (MAKE)
Holzinger, A. (2017) · Machine Learning and Knowledge Extraction · doi:10.3390/make1010001 · Cited by 65 · see citing works
Self-citationImplicit Weight Uncertainty in Neural NetworksPreprint
Pawlowski, N., Brock, A., Lee, M. C. H., Rajchl, M., & Glocker, B. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.01297 · Cited by 59 · see citing works
Turkay, Ç., Jeanquartier, F., Holzinger, A., & Hauser, H. (2014) · Lecture notes in computer science · doi:10.1007/978-3-662-43968-5_7 · Cited by 54 · see citing works
Self-citationOn the Analysis and Interpretation of Inhomogeneous Quadratic Forms as Receptive Fields
Berkes, P., & Wiskott, L. (2006) · Neural Computation · doi:10.1162/neco.2006.18.8.1868 · Cited by 40 · see citing works
Guide to Latin in International Law
Fellmeth, A. X., & Horwitz, M. (2021) · Oxford University Press eBooks · doi:10.1093/acref/9780197583104.001.0001 · Cited by 32 · see citing works
Augmenting Statistical Data Dissemination by Short Quantified Sentences of Natural Language
Hudec, M., Bednárová, E., & Holzinger, A. (2018) · Journal of Official Statistics · doi:10.2478/jos-2018-0048 · Cited by 29 · see citing works
Self-citationInterpretable & Explorable Approximations of Black Box Models
Lakkaraju, H., Kamar, E., Caruana, R., & Leskovec, J. (2017) · arXiv · doi:10.48550/arxiv.1707.01154 · Cited by 23 · see citing works
An Introduction to Deep Visual ExplanationPreprint
Babiker, H. K. B., & Goebel, R. G. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.09482 · Cited by 15 · see citing works
Speculative Abductive Reasoning for Hierarchical Agent Systems
Ma, J., Broda, K. B., Goebel, R. G., et al. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-14977-1_7 · Cited by 12 · see citing works
Knowledge Discovery from Complex High Dimensional Data
Lee, S., & Holzinger, A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-41706-6_7 · Cited by 11 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 4
- Gal, Y. (2016). Uncertainty in deep learning. Cambridge, England: University of Cambridge.
- Kieseberg, P., Weippl, E., & Holzinger, A. (2016). Trust for the doctor‐in‐the‐loop In European Research Consortium for Informatics and Mathematics (ERCIM) news: Tackling big data in the life sciences (Vol. 104, pp. 32– 33). Sophia Antipolis, France: European Research Consortium for Informatics and Mathematics.
- McCarthy, J. (1960). Programs with common sense (pp. 75– 91). RLE and MIT Computation Center.
- Popper, K. (1935). Die Logik der Forschung. Zur Erkenntnistheorie der modernen Naturwissenschaft. Wien, Austria: Springer‐Verlag.
Cited by
Sorted by citations
On the Opportunities and Risks of Foundation Models
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv · doi:10.48550/arxiv.2108.07258 · Cited by 2,676 · see citing works
A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI
Tjoa, E., & Guan, C. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3027314 · Cited by 2,524 · see citing works
Machine Learning Interpretability: A Survey on Methods and Metrics
Carvalho, D. V., Pereira, E. M., & Cardoso, J. S. (2019) · Electronics · doi:10.3390/electronics8080832 · Cited by 1,842 · see citing works
Shin, D. (2021) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2020.102551 · Cited by 1,548 · see citing works
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications
Samek, W., Montavon, G., Lapuschkin, S., Anders, C. J., & Muller, K.-R. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3060483 · Cited by 1,477 · see citing works
“An important application area is in medicine [74].”
BackgroundArtificial intelligence, machine learning and deep learning in advanced robotics, a review
Soori, M., Arezoo, B., & Dastres, R. (2023) · Cognitive Robotics · doi:10.1016/j.cogr.2023.04.001 · Cited by 1,131 · see citing works
Explainable machine learning for scientific insights and discoveries
Roscher, R., Bohn, B., Duarte, M. F., & Garcke, J. (2020) · IEEE Access · doi:10.1109/access.2020.2976199 · Cited by 1,040 · see citing works
Explainable artificial intelligence: a comprehensive review
Minh, D., Wang, H. X., Li, Y. F., & Nguyen, T. N. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-021-10088-y · Cited by 983 · see citing works
A governance model for the application of AI in health care
Reddy, S., Allan, S., Coghlan, S., & Cooper, P. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz192 · Cited by 979 · see citing works
Predicting cancer outcomes with radiomics and artificial intelligence in radiology
Bera, K., Braman, N., Gupta, A., Velcheti, V., & Madabhushi, A. (2022) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-021-00560-7 · Cited by 819 · see citing works
A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images
Chouhan, V., Singh, S. K., Khamparia, A., Gupta, D., Tiwari, P., Moreira, C., Damaševičius, R., & de Albuquerque, V. H. C. (2020) · Applied Sciences · doi:10.3390/app10020559 · Cited by 753 · see citing works
Yang, G., Ye, Q., & Xia, J. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.07.016 · Cited by 717 · see citing works
Langer, M., Oster, D., Speith, T., Hermanns, H., Kästner, L., Schmidt, E., Sesing, A., & Baum, K. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103473 · Cited by 652 · see citing works
Antoniadi, A. M., Du, Y., Guendouz, Y., Wei, L., Mazo, C., Becker, B. A., & Mooney, C. (2021) · Applied Sciences · doi:10.3390/app11115088 · Cited by 629 · see citing works
Evaluating the quality of machine learning explanations: a survey on methods and metrics
Zhou, J., Gandomi, A. H., Chen, F., & Holzinger, A. (2021) · Electronics · doi:10.3390/electronics10050593 · Cited by 606 · see citing works
Self-citationNotions of explainability and evaluation approaches for explainable artificial intelligence
Vilone, G., & Longo, L. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.05.009 · Cited by 598 · see citing works
Lauritsen, S. M., Kristensen, M., Olsen, M. V., Larsen, M. S., Lauritsen, K. M., Jørgensen, M. J., Lange, J., & Thiesson, B. (2020) · Nature Communications · doi:10.1038/s41467-020-17431-x · Cited by 448 · see citing works
Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research
Zhang, Z., Hamadi, H. A., Damiani, E., Yeun, C. Y., & Taher, F. (2022) · IEEE Access · doi:10.1109/access.2022.3204051 · Cited by 450 · see citing works
Interpretability of machine learning-based prediction models in healthcare
Stiglic, G., Kocbek, P., Fijacko, N., Zitnik, M., Verbert, K., & Cilar, L. (2020) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1379 · Cited by 541 · see citing works
Holzinger, A., Carrington, A., & Müller, H. (2020) · KI - Künstliche Intelligenz · doi:10.1007/s13218-020-00636-z · Cited by 406 · see citing works
Self-citationEvaluating XAI: A comparison of rule-based and example-based explanations
van der Waa, J., Nieuwburg, E., Cremers, A., & Neerincx, M. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2020.103404 · Cited by 393 · see citing works
Holzinger, A., Malle, B., Saranti, A., & Pfeifer, B. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.01.008 · Cited by 352 · see citing works
Self-citationBarriers to and facilitators of artificial intelligence adoption in health care: scoping review
Hassan, M., Kushniruk, A., & Borycki, E. (2024) · JMIR Human Factors · doi:10.2196/48633 · Cited by 324 · see citing works
Chen, H., Gomez, C., Huang, C.-M., & Unberath, M. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00699-2 · Cited by 300 · see citing works
Chou, Y.-L., Moreira, C., Bruza, P., Ouyang, C., & Jorge, J. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.11.003 · Cited by 302 · see citing works
The role of agentic AI in shaping a smart future: A systematic review
Hosseini, S., & Seilani, H. (2025) · Array · doi:10.1016/j.array.2025.100399 · Cited by 280 · see citing works
Amann, J., Vetter, D., Blomberg, S. N., Christensen, H. C., Coffee, M., Gerke, S., Gilbert, T. K., Hagendorff, T., Holm, S., Livne, M., et al. (2022) · PLOS Digital Health · doi:10.1371/journal.pdig.0000016 · Cited by 251 · see citing works
“Furthermore, what constitutes a good explanation depends on who is interacting with it [18,19,21–24,37] and there is no quantitative way of determining the most useful interaction in advance [19,36,48,49].”
BackgroundHarnessing the Power of Clinical Decision Support Systems: Challenges and Opportunities
Chen, Z., Liang, N., Zhang, H., Li, H., Yang, Y., Zong, X., Chen, Y., Wang, Y., & Shi, N. (2023) · Open Heart · doi:10.1136/openhrt-2023-002432 · Cited by 244 · see citing works
Precision nutrition: A systematic literature review
Kirk, D., Catal, C., & Tekinerdogan, B. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104365 · Cited by 238 · see citing works
Mienye, I. D., Obaido, G., Jere, N., Mienye, E., Aruleba, K., Emmanuel, I. D., & Ogbuokiri, B. (2024) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2024.101587 · Cited by 212 · see citing works
Human-centered XAI: Developing design patterns for explanations of clinical decision support systems
Schoonderwoerd, T. A. J., Jorritsma, W., Neerincx, M. A., & van den Bosch, K. (2021) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2021.102684 · Cited by 210 · see citing works
Resnik, D. B., & Hosseini, M. (2025) · AI and Ethics · doi:10.1007/s43681-024-00493-8 · Cited by 228 · see citing works
“An alternative to the non-solution approach is to make AI explainable [11, 96, 151, 186].”
Naiseh, M., Al-Thani, D., Jiang, N., & Ali, R. (2023) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2022.102941 · Cited by 170 · see citing works
Feigerlova, E., Hani, H., & Hothersall-Davies, E. (2025) · BMC Medical Education · doi:10.1186/s12909-025-06719-5 · Cited by 166 · see citing works
Operationalising ethics in artificial intelligence for healthcare: a framework for AI developers
Solanki, P., Grundy, J., & Hussain, W. (2023) · AI and Ethics · doi:10.1007/s43681-022-00195-z · Cited by 157 · see citing works
“Adhere to interpretability based on regulatory and efficiency demands and users’ needs [118, 119, 122-125] 14.”
BackgroundA Clinician's Guide to Artificial Intelligence: How to Critically Appraise Machine Learning Studies
Faes, L., Liu, X., Wagner, S. K., Fu, D. J., Balaskas, K., Sim, D. A., Bachmann, L. M., Keane, P. A., & Denniston, A. K. (2020) · Translational Vision Science & Technology · doi:10.1167/tvst.9.2.7 · Cited by 162 · see citing works
Fairness and Explanation in AI-Informed Decision Making
Angerschmid, A., Zhou, J., Theuermann, K., Chen, F., & Holzinger, A. (2022) · Machine Learning and Knowledge Extraction · doi:10.3390/make4020026 · Cited by 161 · see citing works
Self-citationSolving the Explainable AI Conundrum by Bridging Clinicians’ Needs and Developers’ Goals
Bienefeld, N., Boss, J. M., Lüthy, R., Brodbeck, D., Azzati, J., Blaser, M., Willms, J., & Keller, E. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00837-4 · Cited by 159 · see citing works
Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond
Nasir, S., Khan, R. A., & Bai, S. (2024) · IEEE Access · doi:10.1109/access.2024.3369912 · Cited by 152 · see citing works
The explainability paradox: Challenges for xAI in digital pathology
Evans, T., Retzlaff, C. O., Geißler, C., Kargl, M., Plass, M., Müller, H., Kiehl, T.-R., Zerbe, N., & Holzinger, A. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.03.009 · Cited by 135 · see citing works
Self-citationBreastScreening-AI: Evaluating medical intelligent agents for human-AI interactions
Calisto, F. M., Santiago, C., Nunes, N., & Nascimento, J. C. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102285 · Cited by 125 · see citing works
Bridging the “last mile” gap between AI implementation and operation: “data awareness” that matters
Cabitza, F., Campagner, A., & Balsano, C. (2020) · Annals of Translational Medicine · doi:10.21037/atm.2020.03.63 · Cited by 113 · see citing works
The Ten Commandments of Ethical Medical AI
Muller, H., Mayrhofer, M. T., Van Veen, E.-B., & Holzinger, A. (2021) · Computer · doi:10.1109/mc.2021.3074263 · Cited by 102 · see citing works
Self-citationKnop, M., Weber, S., Mueller, M., & Niehaves, B. (2022) · JMIR Human Factors · doi:10.2196/28639 · Cited by 103 · see citing works
Explainability and causability in digital pathology
Plass, M., Kargl, M., Kiehl, T., Regitnig, P., Geißler, C., Evans, T., Zerbe, N., Carvalho, R., Holzinger, A., & Müller, H. (2023) · The Journal of Pathology Clinical Research · doi:10.1002/cjp2.322 · Cited by 99 · see citing works
Self-citationExplainable AI and Multi-Modal Causability in Medicine
Holzinger, A. (2021) · i-com · doi:10.1515/icom-2020-0024 · Cited by 87 · see citing works
Self-citationExplainable Artificial Intelligence: a Systematic Review
Vilone, G., & Longo, L. (2020) · arXiv · doi:10.48550/arxiv.2006.00093 · Cited by 269 · see citing works
Groen, A. M., Kraan, R., Amirkhan, S. F., Daams, J. G., & Maas, M. (2022) · European Journal of Radiology · doi:10.1016/j.ejrad.2022.110592 · Cited by 89 · see citing works
Explaining Machine Learning Models for Clinical Gait Analysis
Slijepcevic, D., Horst, F., Lapuschkin, S., Horsak, B., Raberger, A.-M., Kranzl, A., Samek, W., Breiteneder, C., Schöllhorn, W. I., & Zeppelzauer, M. (2022) · ACM Transactions on Computing for Healthcare · doi:10.1145/3474121 · Cited by 82 · see citing works
Shaban-Nejad, A., Michalowski, M., Brownstein, J., & Buckeridge, D. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3088832 · Cited by 71 · see citing works
Bukowski, M., Farkas, R., Beyan, O., Moll, L., Hahn, H., Kiessling, F., & Schmitz-Rode, T. (2020) · European Radiology · doi:10.1007/s00330-020-06874-x · Cited by 67 · see citing works
AI system engineering key challenges and lessons learned
Fischer, L., Ehrlinger, L., Geist, V., Ramler, R., Sobiezky, F., Zellinger, W., Brunner, D., Kumar, M., & Moser, B. (2020) · Machine Learning and Knowledge Extraction · doi:10.3390/make3010004 · Cited by 71 · see citing works
Amugongo, L. M., Kriebitz, A., Boch, A., & Lütge, C. (2025) · AI and Ethics · doi:10.1007/s43681-023-00331-3 · Cited by 68 · see citing works
Machine learning-based clinical decision support using laboratory data
Çubukçu, H. C., Topcu, D. İ., & Yenice, S. (2024) · Clinical Chemistry and Laboratory Medicine (CCLM) · doi:10.1515/cclm-2023-1037 · Cited by 66 · see citing works
The Next Generation of Medical Decision Support: A Roadmap Toward Transparent Expert Companions
Bruckert, S., Finzel, B., & Schmid, U. (2020) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2020.507973 · Cited by 66 · see citing works
Pathologist validation of a machine learning–derived feature for colon cancer risk stratification
L’Imperio, V., Wulczyn, E., Plass, M., Müller, H., Tamini, N., Gianotti, L., Zucchini, N., Reihs, R., Corrado, G. S., Webster, D. R., et al. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.54891 · Cited by 57 · see citing works
Self-citationDissecting scientific explanation in AI (sXAI): A case for medicine and healthcare
Durán, J. M. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103498 · Cited by 62 · see citing works
Truong, N. M., Vo, T. Q., Tran, H. T. B., Nguyen, H. T., & Pham, V. N. H. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e22653 · Cited by 56 · see citing works
The need to move away from agential-AI: Empirical investigations, useful concepts and open issues
Cabitza, F., Campagner, A., & Simone, C. (2021) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2021.102696 · Cited by 55 · see citing works
Suh, J., Yoo, S., Park, J., Cho, S. Y., Cho, M. C., Son, H., & Jeong, H. (2020) · BJU International · doi:10.1111/bju.15122 · Cited by 45 · see citing works
Ma, S., Chen, Q., Wang, X., Zheng, C., Peng, Z., Yin, M., & Ma, X. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713423 · Cited by 48 · see citing works
Tao, L., Zhang, C., Zeng, L., Zhu, S., Li, N., Li, W., Zhang, H., Zhao, Y., Zhan, S., & Ji, H. (2020) · JMIR Medical Informatics · doi:10.2196/16912 · Cited by 42 · see citing works
Rezaeian, O., Bayrak, A. E., & Asan, O. (2026) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2025.2539458 · Cited by 39 · see citing works
Rezaeian, O., Bayrak, A. E., & Asan, O. (2025) · arXiv · doi:10.48550/arxiv.2501.16693 · Cited by 39 · see citing works
Artificial Intelligence in Cardiovascular Imaging: “Unexplainable” Legal and Ethical Challenges?
Lang, M., Bernier, A., & Knoppers, B. M. (2022) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2021.10.009 · Cited by 37 · see citing works
Boztepe, H., Akdeniz Kudubeş, A., Semerci Şahin, R., Durmuş Sarıkahya, S., & Çınar Özbay, S. (2025) · Nurse Education in Practice · doi:10.1016/j.nepr.2025.104568 · Cited by 38 · see citing works
Müller, H., & Holzinger, A. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103546 · Cited by 30 · see citing works
Self-citationExploring explainable AI features in the vocal biomarkers of lung disease
Chen, Z., Liang, N., Li, H., Zhang, H., Li, H., Yan, L., Hu, Z., Chen, Y., Zhang, Y., Wang, Y., et al. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108844 · Cited by 26 · see citing works
Boussi Rahmouni, H., Hassine, N. B. E. H., Chouchen, M., Ceylan, H. İ., Muntean, R. I., Bragazzi, N. L., & Dergaa, I. (2025) · Healthcare · doi:10.3390/healthcare13202553 · Cited by 25 · see citing works
Fox, S., & Rey, V. F. (2024) · Machine Learning and Knowledge Extraction · doi:10.3390/make6030071 · Cited by 22 · see citing works
“…speci fi ed level of causal understanding with e ff ectiveness, e ffi ciency and satisfaction in a speci fi ed context of use”. has been extent to which an explanation of a statement to a human expert achieves of causal understanding with effectiveness, efficiency and satisfaction context of use” [63].”
BackgroundDalal, S., Tilwani, D., Gaur, M., Jain, S., Shalin, V. L., & Sheth, A. P. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3483577 · Cited by 19 · see citing works
Explainable AI (XAI) In Biomedical Signal and Image Processing: Promises and Challenges
Yang, G., Rao, A., Fernandez-Maloigne, C., Calhoun, V., & Menegaz, G. (2022) · 2022 IEEE International Conference on Image Processing (ICIP) · doi:10.1109/icip46576.2022.9897629 · Cited by 18 · see citing works
Optimal Sepsis Patient Treatment using Human-in-the-loop Artificial Intelligence
Gupta, A., Lash, M. T., & Nachimuthu, S. K. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114476 · Cited by 13 · see citing works
Fair Machine Learning in Healthcare: A Survey
Feng, Q., Du, M., Zou, N., & Hu, X. (2025) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3361836 · Cited by 12 · see citing works
Sun, J., Feng, T., Wang, B., Li, F., Han, B., Chu, M., Gong, F., Yi, Q., Zhou, X., Chen, S., et al. (2025) · The Lancet Digital Health · doi:10.1016/s2589-7500(24)00245-0 · Cited by 9 · see citing works
Arends, B. K. O., McCormick, J. M., van der Harst, P., Heus, P., & van Es, R. (2025) · European Journal of Clinical Investigation · doi:10.1111/eci.14387 · Cited by 5 · see citing works
Zhu, Y., Sui, D., Wang, Z., Hu, X., Gu, L., Qi, Y., Wu, T., Wang, L., Wei, Y., Tang, W., et al. (2026) · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791458 · Cited by 3 · see citing works
“Consequently, interpretability in AI has become a major focus [3, 29].”
AI adoption in healthcare organizations: Spheres of development and the virtue of visible value
Duus, R., Cooray, M., & Lilley, S. (2026) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2026.124541 · Cited by 3 · see citing works
Hudon, A. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1606250 · Cited by 3 · see citing works
Liu, C.-H., Chen, Y.-T., Hsu, C., Chen, H.-L., & Yang, C.-W. (2026) · JMIR Medical Education · doi:10.2196/94951 · Cited by 3 · see citing works
Are numerical or verbal explanations of AI the key to appropriate user reliance and error detection?
Papenkordt, J., Ngonga Ngomo, A.-C., & Thommes, K. (2026) · Behaviour & Information Technology · doi:10.1080/0144929x.2025.2568928 · Cited by 2 · see citing works
“First, several studies show that causality is a fundamental attribute for explainability (Gregor and Benbasat 1999; Holzinger et al. 2019; Lipton 2016).”
Disha, J. F., Dehan, M. F. Z., & Anzum, K. M. T. (2026) · Advanced Intelligent Systems · doi:10.1002/aisy.70487
Alhejji, B., Cunningham, S., Dorney, S., & Palmer, R. (2026) · International Journal of Language & Communication Disorders · doi:10.1111/1460-6984.70322 · Cited by 1 · see citing works
“Furthermore, concerns around model explainability and algorithmic transparency (Holzinger et al. 2019 ), as well as the ethical use of patient speech data including informed consent and data protection (Char et al. 2018 ), warrant careful consideration before integration to clinical practice.”
- Authors
- Holzinger, A., Langs, G., Denk, H., Zatloukal, K., & Müller, H.
- Affiliations
- Medical University of Graz (AT)
- Medical University of Vienna (AT)
- University of Graz (AT)
- Corresponding author
- Holzinger, Andreas
- Venue
- WIREs Data Mining and Knowledge Discovery
- Publisher
- Wiley
- Published
- 2019-04-02
- Volume
- 9
- Issue
- 4
- Pages
- e1312-e1312
- ISSN
- 1942-4787 (ISSN-L)
- 1942-4795
- PMC
- PMC7017860
- OpenAlex
- W2927351257
- Cited by
- 1,885
- References
- 87
- Citations by year
- 2026: 378 · 2025: 372 · 2024: 292 · 2023: 229 · 2022: 242 · 2021: 210 · 2020: 135 · 2019: 27
- FWCI
- 10.09
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Austrian Science Fund (I2714)
- Horizon 2020 Framework Programme (765148, 826078)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- explainable AI, causality, explainability, deep learning interpretability, black-box machine learning, transparency and traceability, human explanations, histopathology, usability
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