How would you design an AI assistant for doctors?

Signal to interviewer

I can ship in high-stakes domains by coupling product scope to explicit evaluation gates and clinician accountability.

Clarify

Define initial clinical task scope: documentation, triage support, or care-plan summaries. Confirm role boundaries across physicians, residents, and coordinators. Align on compliance, auditability, and human decision boundaries.

Approach

Use an eval ladder. Rung one: documentation assistance with strict grounding and citations. Rung two: workflow support with human verification. Higher rungs: constrained recommendation support only after passing accuracy, consistency, and bias thresholds.

Metrics & instrumentation

Primary metric: clinician time saved on approved tasks with stable documentation quality. Secondary metrics: correction burden, specialty adoption, workflow turnaround improvement. Guardrails: unsafe-output flags, missing-context incidents, disagreement with standards. Instrumentation: provenance trails, confidence display, edit history, and clinician feedback loops.

Tradeoffs

Tighter controls reduce risk but can limit utility. Broader scope increases value potential but expands failure surface. Richer context improves relevance but adds governance complexity.

Risks & mitigations

Risk: automation bias; mitigate with uncertainty display and mandatory confirmation on sensitive outputs. Risk: population bias; mitigate with segmented evaluation and equity reviews. Risk: stale context; mitigate with source freshness checks and retrieval validation.

Example

In emergency care, start with note drafting from encounter data and evidence links. Track correction patterns and safety reviews before expanding to discharge support.

90-second version

Launch in rungs: low-risk documentation first, workflow assistance second, recommendations last. Promote only when safety and quality gates pass, with full auditability and clinician control at every stage.