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On the road to explainable AI in drug-drug interactions prediction: A systematic review
Computational and Structural Biotechnology Journal, 2022 Over the past decade, polypharmacy instances have been common in multi-diseases treatment. However, unwanted drug-drug interactions (DDIs) that might cause unexpected adverse drug events (ADEs) in multiple regimens therapy remain a significant issue ...
Thanh Hoa Vo +3 more
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Implications of causality in artificial intelligence
Frontiers in Artificial IntelligenceOver the last decade, investment in artificial intelligence (AI) has grown significantly, driven by technology companies and the demand for PhDs in AI. However, new challenges have emerged, such as the ‘black box’ and bias in AI models.
Luís Cavique
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Proceedings of the Conference on Fairness, Accountability, and Transparency, 2019 Recent work on interpretability in machine learning and AI has focused on the building of simplified models that approximate the true criteria used to make decisions. These models are a useful pedagogical device for teaching trained professionals how to predict what decisions will be made by the complex system, and most importantly how the system might
Mittelstadt, BD, Russell, C, Wachter, S
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The effectiveness of explainable AI on human factors in trust models
Scientific ReportsExplainable AI has garnered significant traction in science communication research. Prior empirical studies have firmly established that explainable AI communication could improve trust in AI and that trust in AI engineers was argued to be an under ...
Justin C. Cheung, Shirley S. Ho
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An Interpretable Machine Vision Approach to Human Activity Recognition using Photoplethysmograph Sensor Data [PDF]
, 2018 The current gold standard for human activity recognition (HAR) is based on the use of cameras. However, the poor scalability of camera systems renders them impractical in pursuit of the goal of wider adoption of HAR in mobile computing contexts ...
Brophy, Eoin +4 more
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InformationResearch into conversationally explainable artificial intelligence (CXAI) aims to emulate the interactive and co-constructive nature of explanations. From the perspective of human-centredness, previous work has shown that AI users prefer conversational ...
Alexander Berman, Christine Howes
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Explainable & Safe Artificial Intelligence in Radiology
Journal of the Korean Society of RadiologyArtificial intelligence (AI) is transforming radiology with improved diagnostic accuracy and efficiency, but prediction uncertainty remains a critical challenge.
Synho Do
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IEEE Access, 2023 Chronic Kidney Disease (CKD) is currently experiencing a growing worldwide incidence and can lead to premature mortality if diagnosed late, resulting in rising costs to healthcare systems.
Pedro A. Moreno-Sanchez
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Neural-symbolic computing: An effective methodology for principled integration of machine learning and reasoning [PDF]
, 2019 Current advances in Artificial Intelligence and machine learning in general, and deep learning in particular have reached unprecedented impact not only across research communities, but also over popular media channels.
Garcez, A. +5 more
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Allergology InternationalBackground: Artificial intelligence (AI) is a promising new technology that has the potential of diagnosing allergic conjunctival diseases (ACDs). However, its development is slowed by the absence of a tailored image database and explainable AI models ...
Michiko Yonehara +22 more
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