# Pay for cancer AI only when it has outcome evidence, then pay properly

Source: https://onco.cc/ideas/idea-data-ai-reimbursement-tied-to-outcomes/  
OnCo record `idea-data-ai-reimbursement-tied-to-outcomes` (Idea). Data CC BY-NC 4.0, attribute "Data from OnCo (onco.cc)"; commercial use needs a licence.

## TL;DR

Health systems would pay for AI tools that have shown in trials that they help patients, and pay nothing for tools that have not, giving makers a reason to run the trials.

## Summary

Reimbursement for AI is haphazard: a few tools have billing codes on weak evidence, most have none, so vendors sell on workflow rather than outcomes. The proposal is a payer policy: a temporary payment for AI under coverage with evidence development while a prospective trial runs, converting to durable payment if outcome evidence is positive and ending if not, with payment levels reflecting demonstrated value. Medicare's coverage decisions for a handful of AI devices are a starting point.

## Fields

- Kind: Idea
- Last checked: 2026-09-08
- Hypothesis: Outcome-conditional reimbursement will increase the number of prospective AI trials started per year and shift the market toward tools with demonstrated benefit.
- Rationale: Payment is the strongest signal to developers; where payers required evidence (e.g., for genomic tests via Medicare's MolDX), evidence generation followed.
- Proposed test: One national payer adopts the policy for two years; count AI trials initiated and tools reaching durable coverage versus the prior period.
- Maturity: speculative
- Actor: payer

## Sources

- Bottleneck evidence (AI that is built but not validated or deployed): Wu et al., How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals (Nature Medicine 2021): https://doi.org/10.1038/s41591-021-01312-x

## Connected records

- ideas: [A dedicated fund for randomised trials of cancer AI with patient outcomes](https://onco.cc/ideas/idea-data-prospective-ai-trials-fund/)
- fronts: [AI & Computation](https://onco.cc/fronts/ai-computation/)
- bottlenecks: [AI that is built but not validated or deployed](https://onco.cc/bottlenecks/b-ai-validation/), [Incentives reward me-too drugs and marginal gains](https://onco.cc/bottlenecks/b-incentive-misalignment/)
- key papers: [How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals](https://onco.cc/key-papers/paper-wu-nat-med/)

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