The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Explainable AI for Healthcare: From Black Box to Interpretable Models
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In the citing paper
“XAI aims to enhance the transparency and interpretability of AI models, providing insights into the decision-making process and enabling clinicians to understand the reasoning behind AI-generated recommendations [23].”
From Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond (2024) · cites this paper as [23]
Citations
Cites
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
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Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
Adadi, A., & Berrada, M. (2018) · IEEE Access · doi:10.1109/access.2018.2870052 · Cited by 6,294
Self-citationUnderstanding deep image representations by inverting them
Mahendran, A., & Vedaldi, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299155 · Cited by 1,827
Distribution-Free Predictive Inference for Regression
Lei, J., G’Sell, M., Rinaldo, A., Tibshirani, R. J., & Wasserman, L. (2018) · Journal of the American Statistical Association · doi:10.1080/01621459.2017.1307116 · Cited by 1,049
Artificial intelligence in drug development: Present status and future prospects
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Patient portals and health apps: Pitfalls, promises, and what one might learn from the other
Baldwin, J. L., Singh, H., Sittig, D. F., & Giardina, T. D. (2016) · Healthcare · doi:10.1016/j.hjdsi.2016.08.004 · Cited by 179
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Learning From Explanations Using Sentiment and Advice in RL
Krening, S., Harrison, B. E., Feigh, K. M., Isbell, C. L., Riedl, M. O., & Thomaz, A. L. (2016) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2016.2628365 · Cited by 83
Hicks, S. A., Eskeland, S. L., Lux, M., de Lange, T., Randel, K. R., Jeppsson, M., Pogorelov, K., Halvorsen, P., & Riegler, M. A. (2018) · ACM Multimedia Systems Conference (MMSys) · doi:10.1145/3204949.3208129 · Cited by 22
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Expert identification of visual primitives used by CNNs during mammogram classification
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Epidemic Outbreak Prediction Using Artificial Intelligence
Adhikari, N. C. D., Alka, A., Kurva, V. K., P, S. S., Nayak, H., Rishav, K., Nayak, A. K., Nayak, S. K., Shaj, V., & Karthikeyan (2018) · International Journal of Computer Science and Information Technology · doi:10.5121/ijcsit.2018.10405 · Cited by 10
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Research works printed but not linked: 3
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Cited by
Sorted by citations
Explainable, trustworthy, and ethical machine learning for healthcare: A survey
Rasheed, K., Qayyum, A., Ghaly, M., Al-Fuqaha, A., Razi, A., & Qadir, J. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106043 · Cited by 372
Applications of Explainable Artificial Intelligence in Diagnosis and Surgery
Zhang, Y., Weng, Y., & Lund, J. (2022) · Diagnostics · doi:10.3390/diagnostics12020237 · Cited by 368
Nazar, M., Alam, M. M., Yafi, E., & Su'ud, M. M. (2021) · IEEE Access · doi:10.1109/access.2021.3127881 · Cited by 312
Artificial intelligence in cancer research, diagnosis and therapy
Elemento, O., Leslie, C., Lundin, J., & Tourassi, G. (2021) · Nature Reviews Cancer · doi:10.1038/s41568-021-00399-1 · Cited by 264
From promise to practice: towards the realisation of AI-informed mental health care
Koutsouleris, N., Hauser, T. U., Skvortsova, V., & De Choudhury, M. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00153-4 · Cited by 251
Metadata
- Authors
- Adadi, A., & Berrada, M.
- Affiliations
- École Normale Supérieure de Fès (MA)
- Corresponding author
- Adadi, Amina
- Venue
- Advances in Intelligent Systems and Computing
- Publisher
- Springer Singapore
- Published
- 2020
- Pages
- 327-337
- ISSN
- 2194-5357 (ISSN-L)
- 2194-5365
- OpenAlex
- W3016087461
- Cited by
- 137
- References
- 17
- Citations by year
- 2026: 4 · 2025: 28 · 2024: 36 · 2023: 27 · 2022: 30 · 2021: 10 · 2020: 2
- FWCI
- 28.14
- Type
- book chapter
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Advances in intelligent systems and computing (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- explainable AI, interpretability, black-box models, artificial intelligence in healthcare, trust in AI, transparency, clinical decision support, clinical decision-making
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