# A roadmap to fair and trustworthy prediction model validation in healthcare

arXiv: https://arxiv.org/abs/2304.03779 · HTML: https://mlsanity.com/paper/2304.03779/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 30 references that are in the map.
- First posted on arXiv 7 Apr 2023.

## Builds on (most cited first, 2 of 2)

- [Learning Optimized Risk Scores](https://mlsanity.com/paper/1610.00168/) (2016)
- [A novel interpretable machine learning system to generate clinical risk scores: An application for predicting early mortality or unplanned readmission in a retrospective cohort study](https://mlsanity.com/paper/2201.03291/) (2022)

## Abstract (arXiv)

A prediction model is most useful if it generalizes beyond the development data with external validations, but to what extent should it generalize remains unclear. In practice, prediction models are externally validated using data from very different settings, including populations from other health systems or countries, with predictably poor results. This may not be a fair reflection of the performance of the model which was designed for a specific target population or setting, and may be stretching the expected model generalizability. To address this, we suggest to externally validate a model using new data from the target population to ensure clear implications of validation performance on model reliability, whereas model generalizability to broader settings should be carefully investigated during model development instead of explored post-hoc. Based on this perspective, we propose a roadmap that facilitates the development and application of reliable, fair, and trustworthy artificial intelligence prediction models.