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Re-focusing explainability in medicine
Digital Health, 2022 Using artificial intelligence to improve patient care is a cutting-edge methodology, but its implementation in clinical routine has been limited due to significant concerns about understanding its behavior. One major barrier is the explainability dilemma
Laura Arbelaez Ossa +5 more
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To explain or not to explain?—Artificial intelligence explainability in clinical decision support systems [PDF]
PLOS Digital Health, 2022 Explainability for artificial intelligence (AI) in medicine is a hotly debated topic. Our paper presents a review of the key arguments in favor and against explainability for AI-powered Clinical Decision Support System (CDSS) applied to a concrete use case, namely an AI-powered CDSS currently used in the emergency call setting to identify patients with
Julia Amann +14 more
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Computer Assisted Methods in Engineering and Science, 2022 The techniques of explainability and interpretability are not alternatives for many realworld problems, as recent studies often suggest. Interpretable machine learning is not a subset of explainable artificial intelligence or vice versa. While the former
Ivars Namatēvs +2 more
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Novel methods for elucidating modality importance in multimodal electrophysiology classifiers
Frontiers in Neuroinformatics, 2023 IntroductionMultimodal classification is increasingly common in electrophysiology studies. Many studies use deep learning classifiers with raw time-series data, which makes explainability difficult, and has resulted in relatively few studies applying ...
Charles A. Ellis +11 more
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EID: Facilitating Explainable AI Design Discussions in Team-Based Settings
International Journal of Crowd Science, 2023 Artificial intelligence (AI) systems have many applications with tremendous current and future value to human society. As AI systems penetrate the aspects of everyday life, a pressing need arises to explain their decision-making processes to build trust ...
Jiehuang Zhang, Han Yu
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Explaining norms and norms explained [PDF]
Behavioral and Brain Sciences, 2009 AbstractOaksford & Chater (O&C) aim to provide teleological explanations of behavior by giving an appropriate normative standard: Bayesian inference. We argue that there is no uncontroversial independent justification for the normativity of Bayesian inference, and that O&C fail to satisfy a necessary condition for teleological explanations:
Danks, David, Eberhardt, Frederick
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The false hope of current approaches to explainable artificial intelligence in health care
The Lancet: Digital Health, 2021 Summary: The black-box nature of current artificial intelligence (AI) has caused some to question whether AI must be explainable to be used in high-stakes scenarios such as medicine.
Marzyeh Ghassemi, PhD +2 more
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To explain or not to explain [PDF]
Proceedings of the 24th International Conference on Intelligent User Interfaces, 2019 Recommender systems have been increasingly used in online services that we consume daily, such as Facebook, Netflix, YouTube, and Spotify. However, these systems are often presented to users as a "black box", i.e. the rationale for providing individual recommendations remains unexplained to users.
Millecamp, Martijn +3 more
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Perturbation-Based Explainable AI for ECG Sensor Data
Applied Sciences, 2023 Deep neural network models have produced significant results in solving various challenging tasks, including medical diagnostics. To increase the credibility of these black-box models in the eyes of doctors, it is necessary to focus on their ...
Ján Paralič +4 more
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Explainability for artificial intelligence in healthcare: a multidisciplinary perspective
BMC Medical Informatics and Decision Making, 2020 Background Explainability is one of the most heavily debated topics when it comes to the application of artificial intelligence (AI) in healthcare. Even though AI-driven systems have been shown to outperform humans in certain analytical tasks, the lack ...
Julia Amann +5 more
doaj +1 more source