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Researchers are testing whether a trained community health worker can collect ultrasound video with a simple, predefined abdominal sweep, while an AI model checks the scan and estimates gestational age. A prospective study in Chicago and Nairobi found the model’s estimates were close to a clinical standard—but this remains task-specific research, not a substitute for comprehensive prenatal ultrasound or routine care.
How does the AI-assisted scan work?
Instead of asking a novice operator to find and measure particular fetal structures in real time, the approach uses a blind sweep: the operator moves the probe across the abdomen in a predefined pattern and records video. The AI assesses the video’s quality and estimates gestational age. The project team has also described fetal-presentation output, but that is distinct from the main published study result.
The research app processed video on a tablet and returned estimates in real time. Google’s project interview says it could do so without Wi-Fi. In the published study, the results were hidden from operators so they would not influence clinical care. These are reported features of a research implementation, not evidence that the system is broadly available in clinics.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesWhat training do operators need?
The project team reports that workers with no previous ultrasound experience can learn the blind-sweep procedure in about eight hours. The published study describes site-specific training: Chicago operators received a handout, verbal instruction, and a demonstration; Nairobi operators received six hours of hands-on training. Those accounts describe different protocols and should not be treated as one standardized training requirement.
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This design shifts some of the work from real-time image interpretation to following a repeatable collection protocol. It does not eliminate the need for training, scan-quality checks, or a clinical pathway for interpreting results and arranging follow-up.
How accurate was the gestational-age estimate?
A prospective diagnostic study involving Northwestern Medicine in Chicago and Jacaranda Health in Nairobi enrolled 2,043 participants overall. The primary analysis covered 385 participants between 16 and 36 weeks’ gestation. The model had been lightly adapted using Chicago data before evaluation at both sites.
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| Primary analysis, JAMA Network Open (2026) | Mean absolute error in gestational-age estimate |
|---|---|
| AI model, both sites | 4.2 days |
| Hadlock clinical standard, both sites | 4.5 days |
| AI model, Chicago | 4.1 days |
| Hadlock clinical standard, Chicago | 4.6 days |
| AI model, Nairobi | 4.3 days |
| Hadlock clinical standard, Nairobi | 4.3 days |
The study reported noninferiority within a prespecified one-day margin for this gestational-age task. The figures do not establish performance for every ultrasound use: the primary evaluation was not a test of comprehensive fetal-anomaly screening or of improved maternal and newborn outcomes. Read the published study in JAMA Network Open for its methods and analysis.
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Why might this approach help expand access?
Conventional ultrasound can be difficult to deliver in remote clinics when equipment is bulky or expensive, power and repairs are unreliable, or trained sonographers are scarce. A handheld probe and tablet-based workflow may reduce some practical barriers; on-device processing may also help where connectivity is limited. Portability alone, however, does not ensure a reliable scan or useful care. Clinics still need trained operators, quality controls, and a way to act on findings.
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Access gaps are the motivation for this work. Google Health cites a figure of up to 50% of pregnant people in low-resource settings not receiving ultrasound screening during pregnancy; the page does not identify the underlying original source for that estimate in the cited material. The same page quotes Northwestern University’s Dr. Amber Watters: “Obstetric ultrasound is necessary to optimize the health of birthing people and their babies, yet it remains unavailable to half of pregnancies worldwide.” See Google Health’s overview of AI ultrasound research.
What has not been established yet?
- Broader clinical uses: The study’s main endpoint was gestational-age estimation. The project’s mention of fetal presentation does not mean the JAMA evaluation validated every output.
- Results in other settings: The evaluation involved two urban clinical sites, a selected analysis cohort, local model adaptation, and a different probe. Performance and workflow need assessment in other populations and care settings.
- Better health outcomes: The reported accuracy comparison does not show that the approach improves maternal or neonatal outcomes.
- Routine availability: Reported real-time and offline capabilities do not establish that the research system is deployed for routine care.
A separate BMJ Open protocol describes planned evaluation of an AI gestational-age system among 969 participants across 13 facilities in Ghana, Kenya, and South Africa. It is a plan for prospective research, not a completed result; see the BMJ Open study protocol.
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Broader priorities and concerns also matter. In a 2025 survey of 176 respondents from 34 countries, gestational age, fetal viability, fetal presentation, and multiple gestation were common uses and priorities for AI-assisted obstetric ultrasound. Respondents who expressed reservations cited concerns such as providers not understanding the technology, misdiagnosis, and cost. Those are reported views, not measured clinical outcomes; see the 2025 survey in Scientific Reports.
What this means for pregnant people
This research explores whether ultrasound information can be collected by a less-specialized worker and assessed by AI, potentially extending services where sonographers are hard to access. It does not support buying a handheld probe or attempting self-scans at home: the equipment in the study was used as clinical research equipment, and unsupervised home use has not been established as safe or suitable.
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