Cost effectiveness

Area: definition. Apply the shared measurement protocol.

Estimated progress toward AI-managed medicine

15% complete — judgment-based estimate as of November 7, 2026. Confidence: low. The scale starts at 0% before ChatGPT's public release in 2022 and ends at 100% when AI manages the field's continuing advancement without human participation. This rates the outcome's contribution to that transition, not the percentage of tasks solved, a measured fraction of the field, or time remaining.

The shared scoring rubric uses five equally weighted dimensions: execution, breadth, agenda-setting, validation, and sustained autonomous improvement. Points for this outcome are 5 + 5 + 0 + 5 + 0 = 15/100, in that order; each dimension has a maximum of 20. These component judgments make the estimate inspectable, but are not independently measured scores.

Reason for the estimate: Economic modeling makes the outcome assessable, but the cited setting did not meet its cost-effectiveness threshold. Autonomous optimization of real patient benefit per dollar is unproven. The dated baseline below supplies the findings and source links used for this assessment. Unmeasured components receive no demonstrated-progress credit; missing evidence does not prove a missing capability.

What would move the score: Record currency/year, AI price, full pathway costs, incremental QALYs and sensitivity analyses; validate model inputs against prospective local data. Higher autonomy scores additionally require AI to select worthwhile work, validate results and improve the process over repeated cycles without human research management, repair or adjudication. Lower the score if replication fails, quality regresses or previously hidden human assistance is found. Preserve this dated estimate when adding future assessments.

Measurement and acceptance rule

Full cost per appropriately managed patient and, where valid, cost per quality-adjusted life-year gained. State assumptions.

Evidence requirements

Evaluate the complete clinical workflow prospectively, preferably with randomized allocation where feasible. Compare with current care, not an artificially weak clinician baseline. Separate AI alone from clinician-plus-AI. Prespecify clinically meaningful differences and non-inferiority margins, use adequate power, and conduct follow-up sufficient to detect delayed harms. A higher detection rate alone does not establish longer survival.

Related milestones

See the area's proposed milestones. Proposed targets are distinct from the dated evidence below.

Baseline as of November 7 2026

Status: Model-dependent economic result. Sources accessed November 7, 2026. This is a documentary baseline, not an independent replication or an assertion of the worldwide best result. An unknown measurement is not zero.

Dated findings

2026 publication: The modeling study concluded that AI screening priced at $40 per examination was not cost-effective at a $100,000-per-QALY willingness-to-pay threshold. Breast screening cost-effectiveness modeling study.

Interpretation and tracking value

This is a modeled setting, not an observed universal price threshold; benefits, downstream costs, utility assumptions and local practice can change the conclusion.

Next comparable measurement

Record currency/year, AI price, full pathway costs, incremental QALYs and sensitivity analyses; validate model inputs against prospective local data. Preserve this dated observation; append future results with their model/system version, denominator, budget and evidence level rather than overwriting it.