Why Explainability Is Central to the BrainScores Platform

In medicine, transparency matters just as much as accuracy. A model that returns the right answer but cannot explain itself asks clinicians to trust it blindly, and blind trust has no place in a neurosurgical workup.

Clinicians need to understand why a tool is pointing them toward a certain area, pattern, or abnormality. They need outputs that can be reviewed, questioned, and interpreted in the full clinical context of the patient in front of them. That is why explainability is central to how we are building BrainScores.

Seeing the evidence, not just the answer

BrainScores is designed to make MRI data more visual, quantitative, and interpretable. Instead of producing a single conclusion, the platform aims to give users a clearer view of how a finding was identified and where it appears within the underlying anatomy.

This includes:

• A way to see the evidence behind a finding.
• A way to inspect the result at the level of the underlying anatomy.
• A way to keep the expert in control of the final interpretation.

Why this matters for clinical adoption

Clinical adoption depends on more than performance alone. Physicians preparing a patient for epilepsy surgery remain accountable for every decision made along the path to treatment, so the tools they rely on need to provide outputs that can be understood, evaluated, and placed in the broader clinical context. Rather than obscuring how a result was reached, AI should help make the underlying information clearer and support well-informed clinical judgment.

At BrainScores, we are committed to bringing quantitative neuroimaging into clinical workflows responsibly, and that commitment sits at the core of how we develop, test, and validate our technology.