# Publicly funded cancer AI must release open weights and model cards

Source: https://onco.cc/ideas/idea-data-open-weights-for-public-funded-ai/  
OnCo record `idea-data-open-weights-for-public-funded-ai` (Idea). Data CC BY-NC 4.0, attribute "Data from OnCo (onco.cc)"; commercial use needs a licence.

## TL;DR

If public or charity money paid to build a cancer AI model, the model itself (not just a paper about it) must be released so others can test, improve and use it.

## Summary

Most publicly funded cancer AI is described in papers but the trained model is never released, so it cannot be independently validated or built on. The proposal makes release of weights, code, a model card and evaluation data (or an evaluation API where data cannot be shared) a condition of grant funding, mirroring open-access and data-sharing policies, with a governed-access route for models with genuine dual-use or privacy concerns.

## Fields

- Kind: Idea
- Last checked: 2026-09-08
- Hypothesis: Open weights will lead to independent external validations for a majority of funded models within two years of release (versus almost none now) and to reuse in downstream tools, increasing the return on public AI funding.
- Rationale: Open-source releases in general machine learning are reproduced, audited and extended within weeks; closed medical models are neither validated nor used beyond the originating lab.
- Proposed test: One funder adopts the policy for a funding cycle; count external validations and downstream uses of funded models at 24 months versus a prior cycle.
- Maturity: speculative
- Actor: philanthropy

## 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

- fronts: [AI & Computation](https://onco.cc/fronts/ai-computation/)
- institutions: [Cancer Research UK](https://onco.cc/institutions/cruk/), [National Cancer Institute (NIH)](https://onco.cc/institutions/nci/)
- bottlenecks: [AI that is built but not validated or deployed](https://onco.cc/bottlenecks/b-ai-validation/), [Preclinical results do not reproduce](https://onco.cc/bottlenecks/b-reproducibility/)
- 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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