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Explainable artificial intelligence in breast cancer detection and risk prediction: A systematic scoping review

Ghasemi, Amirehsan, Hashtarkhani, Soheil, Schwartz, David L., Shaban‐Nejad, Arash·2024·Cancer Innovation
DOI 10.1002/cai2.136PMC PMC11488119
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Abstract

With the advances in artificial intelligence (AI), data‐driven algorithms are becoming increasingly popular in the medical domain. However, due to the nonlinear and complex behavior of many of these algorithms, decision‐making by such algorithms is not trustworthy for clinicians and is considered a black‐box process. Hence, the scientific community has introduced explainable artificial intelligence (XAI) to remedy the problem. This systematic scoping review investigates the application of XAI in breast cancer detection and risk prediction. We conducted a comprehensive search on Scopus, IEEE Explore, PubMed, and Google Scholar (first 50 citations) using a systematic search strategy. The search spanned from January 2017 to July 2023, focusing on peer‐reviewed studies implementing XAI methods in breast cancer datasets. Thirty studies met our inclusion criteria and were included in the analysis. The results revealed that SHapley Additive exPlanations (SHAP) is the top model‐agnostic XAI technique in breast cancer research in terms of usage, explaining the model prediction results, diagnosis and classification of biomarkers, and prognosis and survival analysis. Additionally, the SHAP model primarily explained tree‐based ensemble machine learning models. The most common reason is that SHAP is model agnostic, which makes it both popular and useful for explaining any model prediction. Additionally, it is relatively easy to implement effectively and completely suits performant models, such as tree‐based models. Explainable AI improves the transparency, interpretability, fairness, and trustworthiness of AI‐enabled health systems and medical devices and, ultimately, the quality of care and outcomes.

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Cited by

Sorted by citations

Authors
Ghasemi, A., Hashtarkhani, S., Schwartz, D. L., & Shaban‐Nejad, A.
Affiliations
  • Department of Pediatrics, Center for Biomedical Informatics, College of Medicine University of Tennessee Health Science Center Memphis Tennessee USA
  • Department of Radiation Oncology, College of Medicine University of Tennessee Health Science Center Memphis Tennessee USA
ORCID
Venue
Cancer Innovation
Publisher
Wiley
Published
2024-07-03
Volume
3
Issue
5
ISSN
  • 2770-9191
  • 2770-9183
DOI
10.1002/cai2.136
PMC
PMC11488119
Cited by
161
References
17
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)

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.

What counts as a claim ›How we measure confidence ›How we check this ›

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