Rethinking external validation for the target population: Capturing patient-level similarity with a generative model

Background: External validation is essential for assessing the transportability of predictive models. However, its interpretation is often confounded by differences between external and development populations. This study introduces a framework to distinguish model deficiencies from case-mix effects. Method: We propose a framework that quantifies each external patient's similarity to the development data and measures performance in subgroups with varying levels of alignment to the development di

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ethics.ai (11 May 2026), “Rethinking external validation for the target population: Capturing patient-level similarity with a generative model,” evidence record 4525, https://ethics.ai/record/4525 (originally published by arXiv).

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