BrainScores Is Not a Black Box: Why Explainability Sits at the Core of Our Platform

In medical AI, 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

Our platform is designed to make MRI data more visual and interpretable, helping users see where anomalies may be present and how they relate to a patient's presentation and quality of life. Rather than simply producing a verdict, BrainScores aims to provide a clearer analytical path:

• 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

For AI to be genuinely useful in a clinical setting, it has to be understandable. A physician preparing a patient for epilepsy surgery is accountable for every decision on the path to the operating room. Tools that support that work must make their reasoning legible, not obscure it.

We think that is the only responsible way to bring quantitative neuroimaging into real clinical workflows — and it is the standard we hold ourselves to as we continue to develop, test, and validate BrainScores.