Evidence map›Paper›PMID 41814354›Full record

ReviewReproductive biology and endocrinology : RB&E2026

The potential, perils and pitfalls of Artificial intelligence (AI) in Assisted Reproductive Technologies (ART).

Akanksha Garg, David B Seifer

Abstract readReview
In one paragraph

Review 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. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Akanksha GargObstetrics, Gynecology and Reproductive Sciences, Yale School of Medicine, New Haven, CT, 06510, USA. akanksha.garg@yale.edu.
David B SeiferObstetrics, Gynecology and Reproductive Sciences, Yale School of Medicine, New Haven, CT, 06510, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The applications of Artificial intelligence (AI) have exponentially increased in all aspects of life, including within Assisted Reproductive Technologies (ART). Although there is tremendous promise demonstrated by AI assisted tools, there are concerning limitations and cautions to heed before widespread use is adopted into everyday clinical practice. The aim of this review is to offer healthcare providers and researchers an overview of AI applications and to assist them in independently reviewing and appraising AI research in its application to ART.We review different types of AI technologies while exploring current research of applications of AI within ART. Specific applications include embryo and gamete selection and AI as a clinical decision-making support system. We summarize the benefits of integrating AI into an ART setting and describe the known limitations and challenges of this technology in its application to our missions as clinicians, educators and scholars. We consider the ethical implications of AI within ART and equip clinicians and researchers with a framework to critically appraise, evaluate and review research utilizing AI specifically within the setting of ART. We present current international guidance and governance that can be adapted to publishing and reporting AI research in ART.

Indexed as

Artificial IntelligenceReproductive Techniques, AssistedDecision Support Systems, ClinicalHumansPractice Guidelines as TopicSocieties, MedicalAIAI limitationsARTArtificial IntelligenceAssisted reproductionBenefitsCritical appraisalIVFRisks

Identifiers

PMID41814354
PMCPMC13003707

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.