A survey of obstetric ultrasound uses and priorities for artificial intelligence-assisted obstetric ultrasound in low- and middle-income countries
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 anony...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-87284-1 |
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| author | Amy Sarah Ginsburg Zylee Liddy Eren Alkan Kayla Matcheck Susanne May |
| author_facet | Amy Sarah Ginsburg Zylee Liddy Eren Alkan Kayla Matcheck Susanne May |
| author_sort | Amy Sarah Ginsburg |
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| description | 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. |
| format | Article |
| id | doaj-art-fc9d8ca5125a4cfbac1642c7a6488b31 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-fc9d8ca5125a4cfbac1642c7a6488b312025-02-02T12:16:33ZengNature PortfolioScientific Reports2045-23222025-01-0115111110.1038/s41598-025-87284-1A survey of obstetric ultrasound uses and priorities for artificial intelligence-assisted obstetric ultrasound in low- and middle-income countriesAmy Sarah Ginsburg0Zylee Liddy1Eren Alkan2Kayla Matcheck3Susanne May4Clinical Trials Center, University of WashingtonClinical Trials Center, University of WashingtonCaption Health, GE HealthCareCaption Health, GE HealthCareClinical Trials Center, University of WashingtonAbstract 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.https://doi.org/10.1038/s41598-025-87284-1Obstetric ultrasoundLow- and middle-income countriesPoint of careArtificial intelligence |
| spellingShingle | Amy Sarah Ginsburg Zylee Liddy Eren Alkan Kayla Matcheck Susanne May A survey of obstetric ultrasound uses and priorities for artificial intelligence-assisted obstetric ultrasound in low- and middle-income countries Scientific Reports Obstetric ultrasound Low- and middle-income countries Point of care Artificial intelligence |
| title | A survey of obstetric ultrasound uses and priorities for artificial intelligence-assisted obstetric ultrasound in low- and middle-income countries |
| title_full | A survey of obstetric ultrasound uses and priorities for artificial intelligence-assisted obstetric ultrasound in low- and middle-income countries |
| title_fullStr | A survey of obstetric ultrasound uses and priorities for artificial intelligence-assisted obstetric ultrasound in low- and middle-income countries |
| title_full_unstemmed | A survey of obstetric ultrasound uses and priorities for artificial intelligence-assisted obstetric ultrasound in low- and middle-income countries |
| title_short | A survey of obstetric ultrasound uses and priorities for artificial intelligence-assisted obstetric ultrasound in low- and middle-income countries |
| title_sort | survey of obstetric ultrasound uses and priorities for artificial intelligence assisted obstetric ultrasound in low and middle income countries |
| topic | Obstetric ultrasound Low- and middle-income countries Point of care Artificial intelligence |
| url | https://doi.org/10.1038/s41598-025-87284-1 |
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