ideasIdea

A dedicated fund for randomised trials of cancer AI with patient outcomes

Thousands of cancer AI tools have been tested on old data; almost none in a proper trial. Fund the trials, with endpoints that matter to patients.

Systematic reviews find that fewer than a few percent of published oncology AI models have prospective evaluation and vanishingly few have randomised trials with clinical endpoints. Exceptions such as the MASAI mammography screening trial in Sweden show the design is feasible and informative. The proposal is a public and philanthropic fund that pays for pragmatic randomised trials of AI tools in pathology, radiology, screening and decision support, requiring pre-registration, clinical endpoints (cancer detection rate, interval cancers, time to treatment, survival) and open reporting.

Hypothesis

Randomised evaluation will show that a minority of AI tools with strong retrospective performance improve patient-relevant outcomes, and the resulting evidence will drive adoption of those that do and retirement of those that do not.

Rationale

Retrospective accuracy has repeatedly failed to translate into benefit in other digital health interventions; only randomisation resolves the question, and the MASAI trial shows it can be done at scale within a screening programme.

What would test it

Fund ten pragmatic randomised trials of deployed or near-deployed cancer AI tools over five years; report how many show benefit, harm or no effect.

Maturity

early clinical

Who has to act

philanthropy

Cost to try

Large (over $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 dedicated fund for randomised trials of cancer AI with patient outcomes

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