Evidence map›Paper›PMID 41027574›Full record

ArticleClinical and experimental reproductive medicine2026

Current state of artificial intelligence applications in assisted reproductive technology: A narrative review.

Ju Hee Kim

Abstract read
In one paragraph

Article in Clinical and experimental reproductive 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.

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

1 author.

Ju Hee KimDepartment of Obstetrics and Gynecology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea. xjuheex@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) has rapidly advanced in healthcare, demonstrating significant potential in analyzing large, heterogeneous datasets using optimized algorithms for disease prediction and personalized treatment. Assisted reproductive technology (ART), particularly in vitro fertilization (IVF) and embryo transfer, generates extensive data, making it especially suitable for AI-driven analysis. AI-based applications aim to improve clinical outcomes through personalized ART strategies and predictive algorithms, with potential applications categorized into various procedural stages. Despite its promising nature, most AI-related ART studies appear in general scientific journals rather than core obstetrics and gynecology publications. Moreover, limited clinician understanding of AI methodologies, strengths, and limitations represents a barrier to clinical implementation. This review summarizes recent advancements in AI applications within ART, covering areas such as clinical counseling, outcome prediction, IVF workflow management, controlled ovarian stimulation and follicular monitoring, oocyte and semen analysis, and embryo assessment. It also addresses future considerations for the responsible integration of AI technologies in ART, emphasizing the importance of multidisciplinary collaboration. Integrating AI into ART holds substantial promise and, with targeted research and development, is expected to meaningfully advance the achievement of successful pregnancies.

Indexed as

Artificial intelligenceFertilization in vitroMachine learningReproductive techniques

Identifiers

PMID41027574
PMCPMC13541410

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.