Inside PneumoScan: Making Chest X-Ray AI Explainable with Grad-CAM
How UhAI is using Grad-CAM heatmaps to help clinicians see exactly what our chest X-ray capability is looking at, and why that matters for trust in medical AI.
May 2026 · UhAI Research Team
One of the biggest barriers to clinical trust in AI is the 'black box' problem: a model that returns a confident answer with no explanation of why. PneumoScan, an AI chest X-ray analysis capability being developed inside the uhAI Healthcare Platform, addresses this directly with Gradient-weighted Class Activation Mapping (Grad-CAM).
Grad-CAM produces a visual heatmap overlaid on the original X-ray, highlighting the specific regions that most influenced the model's prediction. For a clinician, this transforms an opaque prediction into a reviewable, verifiable second opinion, they can see whether the model is focusing on clinically plausible regions of the lung, or on artifacts that shouldn't matter.
We believe explainability isn't a nice-to-have for medical AI: it's a prerequisite for responsible deployment. PneumoScan is still in development and not yet clinically verified, but it's being built with this principle from the ground up, and every capability we add to the platform will follow the same standard.