Uncertainty & Confidence Management
A Healthcare AI Benchmark Suite for Uncertainty Recognition, Justification Boundaries, Confidence Calibration, and Safe Action
Healthcare AI should not be rewarded merely for sounding confident or producing a complete answer. Trustworthy reasoning requires staying within the limits of what the available evidence can justify.
A diagnosis may remain uncertain while the safest action is clear. A result may be reliable while causal attribution remains weak. A patient may be medically stable while disposition remains unsafe. Confidence therefore must be attached to a specific object rather than treated as one global score.
Has the AI exceeded what it can justify?
Evidence → Uncertainty → Justification → Confidence → Action → Reassessment
UCM measures whether a model can:
- identify known, unknown, pending, conflicting, and unreliable information;
- distinguish decision-blocking, non-blocking, and conditionally blocking uncertainty;
- define the justification boundary for each conclusion and action;
- calibrate confidence separately for evidence quality, diagnosis, causal attribution, risk, action, prognosis, and disposition;
- act safely without forcing diagnostic closure;
- avoid unnecessary deferral when evidence already supports action;
- select an evidence-proportional destination or intervention;
- revise confidence by exactly the amount new evidence warrants;
- define scheduled and triggered reassessment with owners and thresholds;
- preserve useful residual uncertainty rather than erasing or exaggerating it.
A benchmark that rewards only caution can be defeated by saying that everything is uncertain and escalating every case. UCM deliberately includes cases where uncertainty remains but discharge, home care, de-escalation, outpatient management, or no escalation is justified.
The suite also includes cases where diagnostic confidence is low but emergency action, transfer, monitored care, or a time-limited treatment trial is strongly justified.
This balanced design tests whether the model can earn both its restraint and its decisiveness.
Version 1.0 contains twelve original benchmarks and 360 fictional healthcare source documents across triage, sepsis, medication reconciliation, specialist conflict, interfacility transfer, diagnostic evidence reliability, patient identity, pathology, late results, escalation, prognosis, goals of care, and disposition.