Explainable AI in Healthcare Research

Medical imaging AI has advanced rapidly, but adoption in clinical research depends on more than accuracy. Researchers need to understand why a model reached a particular classification — especially when exploring conditions like Alzheimer's disease where interpretability supports trust and auditability.

At RimansTech, our AlzDetect research platform combines Vision Transformers with explainability techniques such as Grad-CAM to highlight the regions of an MRI scan that most influenced a model's output. This does not replace clinical judgement, but it gives researchers a starting point for discussion and validation.

We see explainability as a design requirement, not an optional overlay. When building decision-support tools in healthcare, transparency should be planned from the architecture stage — including what the model can and cannot claim, how outputs are presented, and what human oversight remains in the loop.