Responsible AI (Healthcare)
Also known as: ethical AI in healthcare, trustworthy AI healthcare, AI fairness in medicine
A set of commitments about how healthcare AI is built and deployed, most useful when reduced to testable checks: representative validation, actionable explainability, named accountability, and ongoing performance measurement.
Responsible AI is frequently stated as values such as fairness, transparency and trustworthiness, which are difficult to fail and therefore difficult to verify. The testable version asks four questions: what population was the model validated on and how does performance break out by subgroup, what does a clinician see alongside a flag so they can judge it during the encounter, who is accountable and what happens when an AI-linked adverse event occurs, and what performance monitoring continues after deployment. Bias in healthcare AI most often enters through the target variable rather than intent, the known example being algorithms that used prior healthcare spending as a proxy for health need and consequently under-identified patients whose care had historically been underfunded.