Explainable AI: current status and future potential
Author:
Funder
HORIZON EUROPE Framework Programme
Publisher
Springer Science and Business Media LLC
Subject
Radiology, Nuclear Medicine and imaging,General Medicine
Link
https://link.springer.com/content/pdf/10.1007/s00330-023-10121-4.pdf
Reference19 articles.
1. Rudin C (2019) Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell 1:206–215. https://doi.org/10.1038/s42256-019-0048-x
2. Litjens G, Kooi T, Bejnordi BE et al (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88. https://doi.org/10.1016/j.media.2017.07.005
3. Adadi A, Berrada M (2018) Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access 6:52138–52160. https://doi.org/10.1109/ACCESS.2018.2870052
4. ACM FAccT. https://facctconference.org/. Accessed 12 Jul 2023
5. Gunning D, Aha DW (2019) DARPA’s explainable artificial intelligence (XAI) program. AI Mag 40:44–58. https://doi.org/10.1609/AIMAG.V40I2.2850
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1. Evaluating the quality of radiomics-based studies for endometrial cancer using RQS and METRICS tools;European Radiology;2024-07-16
2. eXplainable artificial intelligence applied to algorithms for disruption prediction in tokamak devices;Frontiers in Physics;2024-05-14
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