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Explainable AI in Cancer Imaging: Scoping Review of Methods, Modalities, and Clinical Integration.

Dimitris Fotopoulos¹, Ioannis Ladakis¹, Dimitrios Filos¹

¹Laboratory of Computing, Medical Informatics and Biomedical - Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, AUTh Campus, Thessaloniki, 54124, Greece, 30 2310999272.

Journal of Medical Internet Research

|May 20, 2026

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This summary is machine-generated.

Explainable artificial intelligence (xAI) in cancer imaging lacks consistent validation and reporting standards. This review highlights the need for improved methods to ensure trustworthy AI diagnostics in oncology.

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Explainable artificial intelligence (xAI) is crucial for trust in AI-driven cancer diagnostics.
Current evidence on xAI implementation, validation, and reporting in cancer imaging is fragmented.

Purpose of the Study:

To systematically map research on xAI in radiologic cancer imaging.
To summarize methodological and clinical trends and identify gaps in validation and integration.

Main Methods:

A systematic search of PubMed and Scopus (2017-2024) identified 371 peer-reviewed articles.
Studies using machine learning or deep learning with xAI components were analyzed.
Data extraction focused on cancer type, imaging modality, AI/xAI methods, validation, and integration.

Keywords:

cancer imaging explainable AI machine learning medical imaging trustworthiness

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Main Results:

Breast, lung, and brain cancers were most studied, primarily using CT and MRI modalities.
Post hoc xAI methods, especially visualization and feature relevance, dominated the field.
Validation predominantly relied on expert/user-based methods, with rare use of quantitative metrics.
Code availability (17.5%) and decision support system integration (12.1%) were low, limiting clinical deployment.

Conclusions:

Significant methodological inconsistency and insufficient validation exist in xAI for cancer imaging.
Emphasis on visualization over quantitative interpretability hinders reproducibility and clinical trust.
Standardized reporting, robust validation, and user-centered frameworks are essential for trustworthy AI in oncology imaging.