Explainability and Interpretability: Keys to Deep Medicine
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Algorithmic bias and inequitable system behavior are issues in AI-enabled care.
“Equally concerning is the issue of algorithmic bias and inequitable system behavior.”
Without diverse representative and contextually relevant training data, empathic AI systems may misinterpret emotional cues across different cultural, linguistic or socioeconomic groups.
“Without diverse representative and contextually relevant training data, empathic AI systems may misinterpret emotional cues across different cultural, linguistic or socioeconomic groups”
Without diverse representative and contextually relevant training data, agentic AI may generate inaccurate, inequitable, or unsafe recommendations for marginalized populations.
“while agentic AI may generate inaccurate, inequitable, or unsafe recommendations for marginalized populations.5,21,22”
Cites
Explanation in artificial intelligence: Insights from the social sciences
Miller, T. (2019) · Artificial Intelligence · doi:10.1016/j.artint.2018.07.007 · Cited by 5,302 · see citing works
Health intelligence: how artificial intelligence transforms population and personalized health
Shaban-Nejad, A., Michalowski, M., & Buckeridge, D. L. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0058-9 · Cited by 252 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Rahman, A., Hossain, M. S., Muhammad, G., Kundu, D., Debnath, T., Rahman, M., Khan, M. S. I., Tiwari, P., & Band, S. S. (2023) · Cluster Computing · doi:10.1007/s10586-022-03658-4 · Cited by 399 · see citing works
Shaban-Nejad, A., Michalowski, M., Brownstein, J., & Buckeridge, D. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3088832 · Cited by 71 · see citing works
Self-citationGirdwood, T., Kheirinejad, S., Kheirkhah, P., White, B., Davis, R., Schwartz, D., & Shaban-Nejad, A. (2026) · ESMO Real World Data and Digital Oncology · doi:10.1016/j.esmorw.2026.100694 · Cited by 3 · see citing works
Self-citation
- Authors
- Shaban-Nejad, A., Michalowski, M., & Buckeridge, D. L.
- Affiliations
- University of Minnesota (US)
- McGill University (CA)
- Venue
- Studies in Computational Intelligence
- Publisher
- Springer International Publishing
- Published
- 2020-11-03
- Pages
- 1-10
- ISSN
- 1860-949X (ISSN-L)
- 1860-9503
- OpenAlex
- W3095822543
- Cited by
- 35
- References
- 2
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 6 · 2023: 8 · 2022: 10 · 2021: 5
- FWCI
- 9.38
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Studies in computational intelligence (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
- explainability, interpretability, artificial intelligence in healthcare, clinical decision-making, interoperability, electronic health records, health information management system
About these checks
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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