Explainable Artificial Intelligence (XAI) in Diagnostic Imaging for Early Cancer Detection: A Systematic Review of Literature
Keywords:
Medical decision support; early detection of cancer; cancer diagnosis; diagnostic imaging; Explainable artificial intelligence; XAIAbstract
Introduction: Cancer is one of the main causes of mortality worldwide, and the early detection through medical imaging represents a critical factor to improve survival. In this context, Explanatory Artificial Intelligence has emerged as a strategy to overcome the opacity of diagnostic artificial intelligence models.
Objective: To analyze the available scientific evidence on the effectiveness of Explanatory Artificial Intelligence techniques applied to diagnostic imaging for the early detection of various types of cancer.
Material and Methods: A systematic review was carried out in the Scopus and Web of Science databases, following the PRISMA 2020 guidelines and the PICO criteria. In addition, 25 studies published between 2020 and 2025 were selected after quality assessment using QUADAS-2 and ROBINS-I.
Results: Techniques such as Grad-CAM, SHAP and LIME are the most commonly used and are mainly applied in magnetic resonance imaging, computed tomography, mammography and dermoscopy for lung, breast, brain and skin cancer. In multiple studies, explainable models matched or outperformed "black box" systems, achieving high diagnostic accuracy metrics, reducing false positives, and improving clinical confidence by allowing algorithmic decisions to be visually validated.
Conclusions: Explainable artificial intelligence not only increases interpretability, but also strengthens the performance and clinical acceptability of medical artificial intelligence, positioning itself as an essential component for the safe and effective implementation of AI-assisted cancer diagnostic systems in contemporary clinical practice and in future digitalized healthcare environments.
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