Evidence map›Paper›PMID 42618925›Full record

ArticleReproductive biology and endocrinology : RB&E2026

A proposal to meet specific criteria before fully integrating Artificial Intelligence (AI) into the IVF lab.

David B Seifer, Charles L Bormann

Abstract readEditorial
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

David B SeiferYale School of Medicine, 333 Cedar Street, New Haven, CT, USA. David.seifer@yale.edu.
Charles L BormannHarvard Medical School, Boston, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Artificial IntelligenceFertilization in VitroHumans

Identifiers

PMID42618925
PMCPMC13488256

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.