  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.
