Explainable AI in Cancer Imaging: Scoping Review of Methods, Modalities, and Clinical Integration
A comprehensive review maps how to build more interpretable and clinically useful AI cancer imaging tools, establishing standards that could accelerate trustworthy AI deployment.
This scoping review of 371 studies identifies that explainable AI in cancer imaging is methodologically fragmented, with most systems emphasizing visualization over quantitative interpretability and few achieving reproducibility or real-world clinical integration. The evidence base provides a roadmap for standardizing xAI reporting and accelerating deployment of trustworthy oncology AI.
What the study was
- Study design
- Systematic scoping review
- Population
- N/A (371 published studies on xAI in cancer imaging)
- Sample size
- 371
- Category
- Diagnostics
- Maturity
- Exploratory
- Journal
- Journal of Medical Internet Research
Why it surfaced
Large-scale scoping review (n=371 studies) with clear clinical relevance for AI standardization in oncology; identifies actionable gaps; JMIR is indexed, high-volume journal.
A plain-language summary of published research — not medical advice. Talk to a clinician about your own care.