ArticleScientific reports2025
A survey of obstetric ultrasound uses and priorities for artificial intelligence-assisted obstetric ultrasound in low- and middle-income countries.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed.
- From concepts to evaluation: mapping approaches to POCUS training assessment in low- and middle-income countries - a systematic scoping review.The ultrasound journal · 2026Article
- AI-enabled obstetric point-of-care ultrasound as an emerging technology in low- and middle-income countries: provider and health system perspectives.BMC pregnancy and childbirth · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Obstetric ultrasound (OBUS) is recommended as part of antenatal care for pregnant individuals worldwide. To better understand current uses of OBUS in low- and middle-income countries and perceptions regarding potential use of artificial intelligence (AI)-assisted OBUS, we conducted an anonymous online global survey. A total of 176 respondents representing 34 countries participated, including 41% physicians, 49% nurses or midwives, and 6% ultrasound technicians. Most had received OBUS training (72%), reported expertise (60%) and confidence (77%) in OBUS use, and had access to ultrasound (85%). Assessment of gestational age, fetal viability, fetal presentation, and multiple gestation were both the most common OBUS uses and among the most highly prioritized for AI-assisted OBUS development. Most respondents noted ultrasound access was important (84%) and agreed that OBUS improves quality of care (98%) and patient outcomes (97%). Of the 34% expressing reservations associated with using AI-assisted OBUS, healthcare providers not understanding the technology (71%), misdiagnosis (62%), and cost (59%) were the most common. Better understanding the OBUS user, the pregnant individual, and the context, and taking care to ensure responsible, sustainable, and inclusive development and use of AI-assisted OBUS will be critical to successful integration and implementation and to increasing access to OBUS.
Indexed as
Identifiers
What OpenQuestion holds
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 OpenQuestion graph.