Keyword

model explainability

Description: The extent to which an artificial intelligence or statistical model’s decisions and outputs can be understood by people in a particular application context.

Primary topic: Explainable Artificial Intelligence (XAI) (22%)

OpenAlex ID: keywords/model-explainability

Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence

2023 · Sajid Ali, Tamer Abuhmed, et al. · Information Fusion · 1,750

Feature Visualization

2017 · Chris Olah, Alexander Mordvintsev, et al. · Distill · 876

Algorithmic fairness in artificial intelligence for medicine and healthcare

2023 · Richard J. Chen, Judy J. Wang, et al. · Nature Biomedical Engineering · 742

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Machine Learning in Environmental Research: Common Pitfalls and Best Practices

2023 · Jun-Jie Zhu, Meiqi Yang, et al. · Environmental Science & Technology · 681

Interpretable Machine Learning

2021 · Valerie Chen, Jeffrey Li, et al. · Queue · 516

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Clinical applications of machine learning algorithms: beyond the black box

2019 · David S Watson, Jenny Krutzinna, et al. · BMJ · 449

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Improving palliative care with deep learning

2017 · Anand Avati, Kenneth Jung, et al. · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · 445

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Deep Learning for Medical Image-Based Cancer Diagnosis

2023 · Xiaoyan Jiang, Zuojin Hu, et al. · Cancers · 346

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Vehicle Trajectory Prediction Using LSTMs With Spatial–Temporal Attention Mechanisms

2021 · Lei Lin, Weizi Li, et al. · IEEE Intelligent Transportation Systems Magazine · 284

Radiomics for precision medicine: Current challenges, future prospects, and the proposal of a new framework

2020 · A. Ibrahim, S. Primakov, et al. · Methods · 266

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A Survey of the State of Explainable AI for Natural Language Processing

2020 · Marina Danilevsky, Kun Qian, et al. · Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the International Joint Conference on Natural Language Processing (AACL-IJCNLP) · 240

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Interpretable survival prediction for colorectal cancer using deep learning

2021 · Ellery Wulczyn, David F. Steiner, et al. · npj Digital Medicine · 229

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A systematic review of natural language processing applied to radiology reports

2021 · Arlene Casey, Emma Davidson, et al. · BMC Medical Informatics and Decision Making · 210

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Explainable and Interpretable Models in Computer Vision and Machine Learning

2018 · Hugo Jair Escalante, Sérgio Escalera, et al. · The Springer series on challenges in machine learning · 199

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Machine Learning for Hypertension Prediction: a Systematic Review

2022 · Gabriel F. S. Silva, Thales P. Fagundes, et al. · Current Hypertension Reports · 191