Explainable AI
Trust through transparency—interpretable signals and clinically meaningful explanations.
Explainability
Interpretable signals, clinically meaningful explanations
We design our systems so that clinicians can understand how the model reasons—from attention and saliency to natural-language or structured summaries that tie outputs to the underlying imaging evidence. Explainability is not an add-on; it is core to clinical trust and appropriate use.
How we explain
Where appropriate, we provide interpretable signals (e.g., region-level relevance or heatmaps) and clinically meaningful explanations that support validation and correction. Our goal is to make AI outputs auditable and communicable—so that clinicians can stand behind every insight and communicate clearly with referring physicians and patients.
Transparency
Auditability and human oversight
Our systems are designed for auditability and transparency: from saliency and attention visualizations to natural-language summaries, we tie model outputs to the underlying imaging evidence. Human oversight and clinician-in-the-loop review are central to how we deploy AI—never as a black box.
Governance
Responsible innovation
We document appropriate use and limitations clearly. We support governance through explainable outputs, logged overrides, and alignment with emerging expectations around AI in healthcare. Our commitment is to innovation with responsibility and measurable impact.