ArticleNPJ digital medicine2026
Trial emulation for validating the clinical efficacy of a foundational AI model in embryo selection.
Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
7 authors.
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Abstract
AI models for embryo selection often rely on correlational metrics that ignore clinical confounders. We present a target trial emulation framework to approximate causal effects for a foundational AI model, FEMI, for non-invasive embryo assessment using multi-center trial emulation (n = 4674). Propensity score matching established a robust association between FEMI-Ploidy and implantation failure showing an average treatment effect (ATE) of -0.131 (95% CI [-0.196, -0.066]) in the development cohort and -0.157 (95% CI [-0.254, -0.054]) in the external cohort. Comparative efficacy using S-Learner models demonstrated that a high-risk FEMI score carried a significantly stronger individual treatment effect (ITE) penalty on implantation compared to other scoring mechanisms (p < 0.0001). This superiority persisted after adjusting for maternal age, suggesting FEMI captures unique biological features. This causal framework establishes a rigorous standard for AI validation in IVF, providing the necessary pre-clinical justification for prospective randomized controlled trials.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.