Researchers are testing an approach in which a health worker sweeps a handheld ultrasound probe across the abdomen in a set pattern, then an AI model checks the video and estimates gestational age. In a two-site study in Chicago and Nairobi, the model’s estimate was noninferior to a clinical standard—but this is task-specific research, not a substitute for comprehensive prenatal ultrasound or routine care.
How the AI-assisted scan works
Rather than asking an operator to find and measure particular fetal structures in real time, the method uses a predefined “blind sweep” across the abdomen. The worker records ultrasound video by following the protocol; software then assesses scan quality and estimates gestational age. The project team has also described fetal-presentation output, but that is distinct from the primary result evaluated in the published study.
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In the study workflow, a tablet processed video and returned estimates in real time. Google’s interview says processing can work without Wi-Fi, a potentially useful capability where connectivity is unreliable. In the published study, operators could not see diagnostic results, so the estimates would not influence clinical care during the evaluation.
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Who collects the scan, and what training is involved?
The approach is intended to let workers with limited or no previous ultrasound experience collect usable video. Google’s account says workers can learn the procedure in about eight hours, while the study reports training that differed by site: Chicago operators received a handout, verbal instruction, and demonstration; Nairobi operators received six hours of hands-on training. These are different descriptions of training, not a single uniform protocol.
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Standardized scanning matters because AI does not remove the need to acquire an adequate image. The operator must follow the sweep protocol, the system must assess video quality, and a clinical pathway must be available if results need follow-up.
What the Chicago–Nairobi study found
A prospective diagnostic study by Google researchers, Northwestern Medicine, and Jacaranda Health enrolled 2,043 participants across the two sites. Its primary analysis included 385 participants at 16 to 36 weeks’ gestation. The model had been lightly adapted using Chicago data before evaluation at both locations.
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| Site or analysis | AI model mean absolute error | Hadlock clinical standard mean absolute error |
|---|---|---|
| Chicago | 4.1 days | 4.6 days |
| Nairobi | 4.3 days | 4.3 days |
| Primary analysis across both sites | 4.2 days | 4.5 days |
In this study’s primary analysis, gestational-age estimates met the prespecified noninferiority criterion: the model’s mean absolute error was within a one-day margin of the Hadlock clinical standard. The finding is promising for this particular task and study population; it does not establish accuracy for every ultrasound use or every setting. Read the study in JAMA Network Open.
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Access barriers can include bulky or costly conventional equipment, unreliable power and repair services, and too few trained sonographers. Handheld probes may be easier to transport and may run on battery power, while on-device video processing can reduce reliance on a network connection. Those features could make selected ultrasound information more reachable in clinics with limited infrastructure, but portability alone does not ensure a reliable scan or appropriate follow-up.
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Google Health cites a figure that up to 50% of pregnant people in low-resource settings do not receive ultrasound screening during pregnancy. The page presents this as background information, but it does not identify the underlying original source, so it should be treated as Google’s cited estimate rather than a newly verified global measurement. Google Health’s overview of ultrasound research also quotes Northwestern’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.”
What the evidence does—and does not—show
- It evaluates gestational-age estimation. The primary study result concerns an AI estimate from blind-sweep video compared with the Hadlock clinical standard.
- It does not validate a complete prenatal scan. The evaluation was not a test of comprehensive fetal-anomaly screening or of improved maternal and newborn outcomes. The project’s description of fetal-presentation output should not be read as proof that every output was validated by the primary endpoint.
- It comes from two urban clinical sites. The study used a selected analysis cohort, local model adaptation, and a different probe; other populations, equipment, and care workflows need evaluation.
- It is not evidence for self-scanning. The probes were clinical research equipment, and the study does not establish that unsupervised home scans are appropriate.
A 2025 survey of 176 respondents from 34 countries found gestational age, fetal viability, fetal presentation, and multiple gestation among common uses and priorities for AI-assisted obstetric ultrasound. Respondents who raised concerns cited risks such as providers not understanding the technology, misdiagnosis, and cost. These are reported views, not measured outcomes of the Chicago–Nairobi study. The survey appears in Scientific Reports.
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What comes next
A separate study protocol plans a prospective evaluation of an AI gestational-age system with 969 participants at 13 facilities in Ghana, Kenya, and South Africa. Because it is a protocol, those figures describe planned research, not completed results. Broader validation will help establish whether the approach works in other settings and fits local clinical pathways. See the protocol in BMJ Open.
For pregnant people, the practical takeaway is that researchers are testing a way to extend access to one kind of ultrasound information—not a replacement for a clinician’s assessment or recommended prenatal scans. Google’s interview explains why menstrual history alone may not reliably establish pregnancy timing: “Many people have irregular periods or don’t track their periods, so we can’t always rely on period history to estimate gestational age.” Read the Google interview.
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