ideasIdea

A standard evaluation pathway for AI-assisted pathology, from reader study to deployment

Agree one recipe for proving a pathology AI helps: first a controlled study with many pathologists and cases, then a real-world trial with turnaround, accuracy and cost measured.

Pathology AI is cleared on varied evidence, often without showing that pathologists using it perform better than without. The proposal is a standard two-stage pathway: a multi-reader multi-case study with a fully crossed design, pre-registered and adequately powered, measuring pathologist-plus-AI versus pathologist alone on diagnostic accuracy and time; then a prospective deployment study measuring turnaround, discordance at tumour boards, and downstream treatment changes, all reported to the registry.

Hypothesis

Applying the standard will show that a minority of pathology AI tools improve pathologist accuracy in a fully crossed design, and those that do will show measurable turnaround and treatment-decision benefits in deployment.

Rationale

Radiology has decades of reader-study methodology; pathology AI has borrowed the tools inconsistently. Standardisation makes results comparable and procurement rational.

What would test it

Apply the pathway to five cleared pathology AI tools (for example prostate biopsy detection, HER2 scoring, mitotic counting); publish results in a common format.

Maturity

early clinical

Who has to act

research

Cost to try

Medium ($1M to $50M)

Years to first evidence

Bottlenecks it attacks

AI that is built but not validated or deployed · Thousands of cancer AI models are published; a handful are in clinical use, and fewer have shown they help patients.

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A standard evaluation pathway for AI-assisted pathology, from reader study to deployment

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