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Explainable AI (XAI) in Medical Imaging: Building Physician Trust

Medora Clinical Team

Medical Review Board

January 17, 2026|6 min read

Key Takeaways

  • Explainable AI (XAI) is critical for high-stakes medical decision-making.
  • Saliency maps and radiomics provide quantifiable evidence for AI findings.
  • Transparency helps mitigate the "black box" problem in medical machine learning.
  • Physicians must remain the final arbiter in AI-assisted diagnostic workflows.
"Transparency in AI is not a feature; it is a clinical requirement for patient safety."

The Trust Gap in Clinical AI

In high-stakes environments like radiology, oncology, or pathology, a simple "positive/negative" output from an AI is insufficient for clinical action. Physicians require evidence that can be scrutinized. This requirement has given rise to the essential field of Explainable AI (XAI).

What is XAI?

Explainable AI refers to a suite of techniques that allow humans to understand and trust the results and output created by machine learning algorithms. In medical imaging, this goes beyond simple classification. It involves the generation of evidence that correlates with known clinical pathology.

Common XAI outputs include:

  • Saliency Maps: Visual heatmaps that highlight the specific pixels or regions of interest that influenced the AI's decision.
  • Feature Attribution: Explaining which specific data points (e.g., patient age, smoking history, or specific lab values) were most weighted in the final assessment.
  • Counterfactual Explanations: Showing how the diagnosis would change if certain clinical features were different.

Radiomics: The Quantifiable Evidence

Beyond visual heatmaps, we are now seeing the integration of Radiomics—the extraction of high-dimensional data (features) from medical images. These are quantifiable metrics regarding texture, shape, and intensity that may be imperceptible to the human eye but provide a solid, data-driven foundation for a diagnosis.

Medora’s Approach to Transparency

At Medora, we believe transparency is non-negotiable. Our models are designed to provide not just a report, but a clinical justification for every finding. This allows the physician to remain the final arbiter, using the AI as a powerful lens through which to view patient data more clearly and confidently.

The Future of S-XAI

The next frontier is Self-Explainable AI (S-XAI), where the explanation is baked into the model architecture itself, rather than added as a post-hoc analysis. This ensures that the reasoning provided is truly representative of the model's inner workings, further closing the trust gap.