Care delivery

Area: definition. Apply the shared measurement protocol.

Estimated progress toward AI-managed medicine

25% 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 10 + 5 + 5 + 5 + 0 = 25/100, 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: Reduced reading workload demonstrates useful workflow automation, but end-to-end treatment timeliness and human labor across the care pathway remain unmeasured. 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: Measure actual staff minutes, correction/review time, waits and treatment delays for the full pathway, including additional recalls. 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

Time to appropriate treatment, waiting time, completed visits, and clinician minutes per patient. Include time spent correcting AI output.

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: Measured workload proxy. 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

August 2023: The 2023 MASAI interim analysis reported a 44.3% reduction in screen-reading workload. MASAI interim randomized trial analysis.

Interpretation and tracking value

Reading workload is not clinician minutes per patient or time to appropriate treatment. The gain must be evaluated alongside recall and diagnostic work downstream.

Next comparable measurement

Measure actual staff minutes, correction/review time, waits and treatment delays for the full pathway, including additional recalls. Preserve this dated observation; append future results with their model/system version, denominator, budget and evidence level rather than overwriting it.