Who Cited It

Causability and explainability of artificial intelligence in medicine

2019 · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · 1,851 citations · 5 from inside this corpus

Andreas Holzinger, Georg Langs, Helmut Denk, Kurt Zatloukal, Heimo Müller

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

Causability and explainability of artificial intelligence in medicine (2019)Causability and explainabilit…Learning representations by back-propagating errors (1986)Learning representations by b…Artificial intelligence: a modern approach (1995)Artificial intelligence: a mo…Deep learning in neural networks: An overview (2014)Deep learning in neural netwo…"Why Should I Trust You?" (2016)"Why Should I Trust You?"Advances in Neural Information Processing Systems 29 (2016)Advances in Neural Informatio…Efficient Estimation of Word Representations in Vector Space (2013)Efficient Estimation of Word …Causality (2009)CausalityMachine learning: Trends, perspectives, and prospects (2015)Machine learning: Trends, per…Learning Deep Architectures for AI (2009)Learning Deep Architectures f…Learning Deep Architectures for AI (2009)Learning Deep Architectures f…Methods for interpreting and understanding deep neural networks (2017)Methods for interpreting and …Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Disco… (2017)Unsupervised Anomaly Detectio…Advances in Neural Information Processing Systems 27 (NIPS 2014) (2014)Advances in Neural Informatio…30 years of adaptive neural networks: perceptron, Madaline, and backpropagation (1990)30 years of adaptive neural n…How to Grow a Mind: Statistics, Structure, and Abstraction (2011)How to Grow a Mind: Statistic…Intelligible Models for HealthCare (2015)Intelligible Models for Healt…A model of inexact reasoning in medicine (1975)A model of inexact reasoning …A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI (2020)A Survey on Explainable Artif…On the Opportunities and Risks of Foundation Models (2021)On the Opportunities and Risk…Machine Learning Interpretability: A Survey on Methods and Metrics (2019)Machine Learning Interpretabi…Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications (2021)Explaining Deep Neural Networ…The effects of explainability and causability on perception, trust, and acceptance: Impli… (2020)The effects of explainability…
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What this paper cites, inside the corpus

PaperYearCited
Learning representations by back-propagating errors198631,688
Artificial intelligence: a modern approach199522,262
Deep learning in neural networks: An overview201418,236
"Why Should I Trust You?"201616,210
Advances in Neural Information Processing Systems 29201613,474
Efficient Estimation of Word Representations in Vector Space201311,714
Causality200911,477
Machine learning: Trends, perspectives, and prospects20159,947
Learning Deep Architectures for AI20096,951
Learning Deep Architectures for AI20095,077
Methods for interpreting and understanding deep neural networks20172,763
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Disco…20172,413
Advances in Neural Information Processing Systems 27 (NIPS 2014)20142,396
30 years of adaptive neural networks: perceptron, Madaline, and backpropagation19902,244
How to Grow a Mind: Statistics, Structure, and Abstraction20111,923
Intelligible Models for HealthCare20151,693
A model of inexact reasoning in medicine19751,450

What cites it, inside the corpus

PaperYearCited
A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI20202,377
On the Opportunities and Risks of Foundation Models20212,265
Machine Learning Interpretability: A Survey on Methods and Metrics20191,822
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications20211,424
The effects of explainability and causability on perception, trust, and acceptance: Impli…20201,420

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Topics

Explainable Artificial Intelligence (XAI)Computer Science
Machine Learning in HealthcareComputer Science
AI in cancer detectionComputer Science

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