ArticleReproductive biology and endocrinology : RB&E2026
A proposal to meet specific criteria before fully integrating Artificial Intelligence (AI) into the IVF lab.
Article in Reproductive biology and endocrinology : RB&E, 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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
2 authors.
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Abstract
Artificial intelligence (AI) is generating genuine excitement in reproductive medicine for good reason. The promise of more consistent embryo assessment, reduced inter-observer variability, better outcome prediction and meaningful support for the embryologist's judgment represents a real opportunity to improve care for patients who have already invested enormously in the pursuit of a family. Realizing that promise, however, depends on something the field has not always done well which is insisting on rigorous validation before widespread adoption. This editorial is written in that spirit, as advocates for AI who believe the technology's long-term acceptance by patients, clinicians and embryologists will be determined by how carefully we deploy it now and how we proceed going forward. How will we know when AI is ready to fully deploy in the IVF lab? The answer requires distinguishing between being safe and being safe and effective and the medical community has not yet reached consensus on either standard for most applications. Professional societies will need to lead that process. However, in the meantime, with commercial adoption accelerating and stakeholder incentives not always aligned with patient welfare, we propose criteria as a starting framework for that discussion.
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Registered trials
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.