Evidence map›Paper›PMID 39400647›Full record

ReviewJournal of assisted reproduction and genetics2025

A review of artificial intelligence applications in in vitro fertilization.

Qing Zhang, Xiaowen Liang, Zhiyi Chen

Registry-linked trialAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of assisted reproduction and genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06884930 (Machine Learning-based Evaluation of Pregnancy Success Indicators in Assisted Reproductive Technology), which is not on this map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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.

NCT06884930 active not recruitingnot on this map

Machine Learning-based Evaluation of Pregnancy Success Indicators in Assisted Reproductive Technology (ART) Cycles

TypeobservationalSponsorIRCCS San RaffaeleRan2025 to 2026Enrolled5,000ConditionsInfertility (IVF Patients)
3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Review
  5. Review
  6. 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

3 authors.

Qing Zhang *Key Laboratory of Medical Imaging Precision Theranostics and Radiation Protection, College of Hunan Province, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, China.
Xiaowen Liang *Key Laboratory of Medical Imaging Precision Theranostics and Radiation Protection, College of Hunan Province, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, China.
Zhiyi Chen *Key Laboratory of Medical Imaging Precision Theranostics and Radiation Protection, College of Hunan Province, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, China. zhiyi_chen@usc.edu.cn.ORCID http://orcid.org/0000-0002-5973-4522

Funding

Clinical Research 4310 Program of the Affiliated Changsha Central Hospital of the University of South China 20214310NHYCG06Health Research Project of Hunan Provincial Health Commission W20241010National Natural Science Foundation of China 82102054
6 · The paper itself

Abstract

The field of reproductive medicine has witnessed rapid advancements in artificial intelligence (AI) methods, which have significantly enhanced the efficiency of diagnosing and treating reproductive disorders. The integration of AI algorithms into the in vitro fertilization (IVF) has the potential to represent the next frontier in advancing personalized reproductive medicine and enhancing fertility outcomes for patients. The potential of AI lies in its ability to bring about a new era characterized by standardization, automation, and an improved success rate in IVF. At present, the utilization of AI in clinical practice is still in its early stages and faces numerous ethical, regulatory, and technical challenges that require attention. In this review, we present an overview of the latest advancements in various applications of AI in IVF, including follicular monitoring, oocyte assessment, embryo selection, and pregnancy outcome prediction. The aim is to reveal the current state of AI applications in the field of IVF, their limitations, and prospects for future development. Further studies, which involve the development of comprehensive models encompassing multiple functions and the conduct of large-scale randomized controlled trials, could potentially indicate the future direction of AI advancements in the field of IVF.

Indexed as

Artificial IntelligenceFertilization in VitroReproductive MedicineFemaleHumansOocytesPregnancyPregnancy OutcomePregnancy RateArtificial intelligenceEmbryo selectionFollicular monitoringIn vitro fertilizationReproduction

Identifiers

PMID39400647
PMCPMC11806189

What Socratic holds

Textmetadata
Read underepoch 390

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 Socratic graph.