Results 21 to 30 of about 4,042,759 (279)
WaSP-ECG: A Wave Segmentation Pretraining Toolkit for Electrocardiogram Analysis
Frontiers in Physiology, 2022 IntroductionRepresentation learning allows artificial intelligence (AI) models to learn useful features from large, unlabelled datasets. This can reduce the need for labelled data across a range of downstream tasks.
Rob Brisk +8 more
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Proceedings of the 25th International Conference on Intelligent User Interfaces, 2020 Explainable AI (XAI) has started experiencing explosive growth, echoing the explosive growth that has preceded it of AI becoming used for practical purposes that impact the general public. This spread of AI into the world outside of research labs brings with it pressures and requirements that many of us have perhaps not thought about deeply enough.
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Explainable AI for Healthcare 5.0: Opportunities and Challenges
IEEE Access, 2022 In the healthcare domain, a transformative shift is envisioned towards Healthcare 5.0. It expands the operational boundaries of Healthcare 4.0 and leverages patient-centric digital wellness. Healthcare 5.0 focuses on real-time patient monitoring, ambient
Deepti Saraswat +7 more
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Painting the Black Box White: Experimental Findings from Applying XAI to an ECG Reading Setting
Machine Learning and Knowledge Extraction, 2023 The emergence of black-box, subsymbolic, and statistical AI systems has motivated a rapid increase in the interest regarding explainable AI (XAI), which encompasses both inherently explainable techniques, as well as approaches to make black-box AI ...
Federico Cabitza +5 more
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From ”Explainable AI” to ”Graspable AI”
Proceedings of the Fifteenth International Conference on Tangible, Embedded, and Embodied Interaction, 2021 Since the advent of Artificial Intelligence (AI) and Machine Learning (ML), researchers have asked how intelligent computing systems could interact with and relate to their users and their surroundings, leading to debates around issues of biased AI systems, ML black-box, user trust, user’s perception of control over the system, and system’s ...
Ghajargar, Maliheh +7 more
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Biomedicines, 2022 Diagnostic support tools based on artificial intelligence (AI) have exhibited high performance in various medical fields. However, their clinical application remains challenging because of the lack of explanatory power in AI decisions (black box problem),
Akira Sakai +12 more
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Explainability of Automated Fact Verification Systems: A Comprehensive Review
Applied Sciences, 2023 The rapid growth in Artificial Intelligence (AI) has led to considerable progress in Automated Fact Verification (AFV). This process involves collecting evidence for a statement, assessing its relevance, and predicting its accuracy.
Manju Vallayil +3 more
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Exploring Explainable Artificial Intelligence for Transparent Decision Making [PDF]
E3S Web of Conferences, 2023 Artificial intelligence (AI) has become a potent tool in many fields, allowing complicated tasks to be completed with astounding effectiveness. However, as AI systems get more complex, worries about their interpretability and transparency have become ...
Praveenraj D. David Winster +6 more
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A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems
ACM Trans. Interact. Intell. Syst., 2018 The need for interpretable and accountable intelligent systems grows along with the prevalence of artificial intelligence ( AI ) applications used in everyday life.
Sina Mohseni, Niloofar Zarei, E. Ragan
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Diagnostics, 2021 Background: Although several studies have been launched towards the prediction of risk factors for mortality and admission in the intensive care unit (ICU) in COVID-19, none of them focuses on the development of explainable AI models to define an ICU ...
Vasileios C. Pezoulas +12 more
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