Evidence map›Paper›PMID 41913353›Full record

ArticlePrenatal diagnosis2026

Artificial Intelligence as an Add-On Instrument in Fetal Ultrasound; Sonographers' and Obstetricians' Expectations.

Renée M Smit, Chiara C M M Lap, Rima Arnaout, Anita J Moon-Grady, Monique C Haak

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Prenatal diagnosis, 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

5 authors.

Renée M SmitDepartment of Obstetrics and Gynecology, Division of Fetal Medicine, Leiden University Medical Center, Leiden, the Netherlands.ORCID https://orcid.org/0009-0006-6454-1254
Chiara C M M LapDepartment of Obstetrics and Gynecology, Division of Fetal Medicine, Leiden University Medical Center, Leiden, the Netherlands.ORCID https://orcid.org/0000-0001-8226-9000
Rima ArnaoutDepartment of Medicine, Division of Cardiology, University of California San Francisco, San Francisco, California, USA.ORCID https://orcid.org/0000-0002-7134-0040
Anita J Moon-GradyDepartment of Pediatrics, Division of Cardiology, University of California San Francisco, San Francisco, California, USA.ORCID https://orcid.org/0000-0003-3822-693X
Monique C HaakDepartment of Obstetrics and Gynecology, Division of Fetal Medicine, Leiden University Medical Center, Leiden, the Netherlands.ORCID https://orcid.org/0000-0002-0443-1997

Funding

Toward efficient performance for deep learning on medical imagingR01HL150394 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Rima Arnaout · 2020 to 2026
$6.2M
NHLBI NIH HHS R01 HL150394the National Institutes of Health R01HL150394
6 · The paper itself

Abstract

objectiveArtificial intelligence applications (AIA) in fetal ultrasound are rapidly evolving, yet their integration into routine clinical practice remains limited. This study explores the attitudes, expectations and concerns of obstetric sonographers and gynecologists regarding AIA as a diagnostic aid during fetal anomaly scans.

methodAn online survey was distributed to sonographers, midwives, gynecologists and fetal medicine consultants performing screening and diagnostic fetal ultrasounds in the Netherlands. The survey included demographics, current use of artificial intelligence (AI), and 22 attitudinal statements rated on acceptance, efficiency, safety, and quality. Respondents were grouped by age, AI experience, and scan type.

resultsAmong 151 respondents, 47% were sonographers performing standard anomaly screening and 49% were performing diagnostic anomaly scans at tertiary-level centers. AI was already used by 62%, primarily for automated biometry. Overall, 87% supported AIA use, yet 86% emphasized retaining final clinical authority. Concerns about data security (10%) and software manipulation (12%) were minimal. While 73% of the surveyed respondents believed AIA can improve scan completeness, only 39% expect workload reduction and 70% expect improved anomaly detection.

conclusionObstetric sonographers generally support AIA integration, anticipating a positive impact on screening outcomes. However, human decision-making remains essential. These insights may guide future implementation strategies and offer momentum to influence referral practices.

Indexed as

Artificial IntelligenceAttitude of Health PersonnelUltrasonography, PrenatalAdultFemaleGynecologistsHumansMiddle AgedNetherlandsObstetriciansPregnancySurveys and Questionnairesartificial intelligenceautomatic image analysisfetal ultrasoundsonographerssurvey

Identifiers

PMID41913353
PMCPMC13571797

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.