Cited by 1,885Explainable Artificial Intelligence (XAI)Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Causability and explainability of artificial intelligence in medicine

Open access
Holzinger, Andreas, Langs, Georg, Denk, Helmut, Zatloukal, Kurt, Müller, Heimo·2019·WIREs Data Mining and Knowledge Discovery

Abstract

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 measured
  • 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 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 measured
  • AI 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.”
  • 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].”
  • 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].”

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Research works printed but not linked: 4

  1. Gal, Y. (2016). Uncertainty in deep learning. Cambridge, England: University of Cambridge.
  2. 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.
  3. McCarthy, J. (1960). Programs with common sense (pp. 75– 91). RLE and MIT Computation Center.
  4. Popper, K. (1935). Die Logik der Forschung. Zur Erkenntnistheorie der modernen Naturwissenschaft. Wien, Austria: Springer‐Verlag.

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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
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
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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