Towards trustable machine learning
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The EU directive 2016/680 General Data Protection Regulation states the "right to an explanation" about the output of an algorithm.
“the EU directive 2016/680 General Data Protection Regulation (GDPR) states the “right to an explanation” about the output of an algorithm107.”
From Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare (2020) · cites this paper as [107]
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Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare
Cirillo, D., Catuara-Solarz, S., Morey, C., Guney, E., Subirats, L., Mellino, S., Gigante, A., Valencia, A., Rementeria, M. J., Chadha, A. S., et al. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0288-5 · Cited by 553 · see citing works
Artificially intelligent chatbots in digital mental health interventions: a review
Boucher, E. M., Harake, N. R., Ward, H. E., Stoeckl, S. E., Vargas, J., Minkel, J., Parks, A. C., & Zilca, R. (2021) · Expert Review of Medical Devices · doi:10.1080/17434440.2021.2013200 · Cited by 440 · see citing works
Exploiting epistemic uncertainty of anatomy segmentation for anomaly detection in retinal OCT
Seebock, P., Orlando, J. I., Schlegl, T., Waldstein, S. M., Bogunovic, H., Klimscha, S., Langs, G., & Schmidt-Erfurth, U. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2919951 · Cited by 164 · see citing works
BreastScreening-AI: Evaluating medical intelligent agents for human-AI interactions
Calisto, F. M., Santiago, C., Nunes, N., & Nascimento, J. C. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102285 · Cited by 125 · see citing works
Reliable Prediction Errors for Deep Neural Networks Using Test-Time Dropout
Cortés-Ciriano, I., & Bender, A. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00297 · Cited by 51 · see citing works
- Venue
- Nature Biomedical Engineering
- Published
- 2018-10-10
- Volume
- 2
- Issue
- 10
- Pages
- 709-710
- ISSN
- 2157-846X (ISSN-L)
- OpenAlex
- W4231944115
- Cited by
- 64
- References
- 0
- Citations by year
- 2025: 3 · 2024: 7 · 2023: 5 · 2022: 17 · 2021: 13 · 2020: 12 · 2019: 6
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Nature Biomedical Engineering (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- trustworthy machine learning, machine learning in healthcare, interpretability, robustness, healthcare implementation, clinical adoption, patient trust
- MeSH terms
- Machine Learning, Colonic Polyps, Colonoscopy, Humans, Pattern Recognition, Automated, Risk Factors, Biomarkers
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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