Background Artificial intelligence (AI) has shown potential in improving the performance of screening fetal anomaly ultrasound scans. We aimed to assess the effect of AI on fetal ultrasound scanning, in terms of diagnostic performance, biometry, scan duration, and sonographer cognitive load. Methods This was a randomised, single centre, open label trial in a large teaching hospital. Pregnant participants with fetal congenital heart disease (CHD) and with healthy fetuses were recruited and scanned with both methods. Screening sonographers were recruited from regional hospitals and were randomised to scan with the AI tool or in the standard fashion, blinded to the fetal CHD status. For the AI-assisted scans, the AI models identified and saved 13 standard image planes, and measured four biometrics. Findings 78 pregnant participants (26 with fetal CHD) and 58 sonographers were recruited. The sensitivity and specificity of the AI-assisted scan in detecting fetal malformation was 88.9% and 98.0% respectively, with the standard scan achieving 81.5% and 92.2% (not significant). AI-assisted scans were significantly shorter than standard scans (median 11.4 min vs 19.7 min, p <0.001). Sonographer cognitive load was significantly lower in the AI-assisted group (median NASA TLX score 35.2 vs 46.5, p <0.001). For all biometrics, the AI repeatability and reproducibility was superior to manual measurements. Interpretation AI assistance in the routine fetal anomaly ultrasound scan results in a significant time saving, along with a reduction in sonographer cognitive load, without a reduction in diagnostic performance. Funding The study was funded by an NIHR doctoral fellowship (NIHR301448) and was supported by grants from the Wellcome Trust (IEH Award, 102431), by core funding from the Wellcome Trust/EPSRC Centre for Medical Engineering (WT203148/Z/16/Z), and the London AI Centre for Value Based Healthcare via funding from the Office for Life Sciences.

TD, JM, SB, LV, RW, AF, JH, BK, and RR are co-founders and shareholders of Fraiya Ltd, a University-NHS spinout company that is aiming to commercialise an AI tool for use in the screening obstetric ultrasound scan.

ISRCTN 65824874

The study was funded by an NIHR doctoral fellowship (NIHR301448) and was supported by grants from the Wellcome Trust (IEH Award, 102431), by core funding from the Wellcome Trust/EPSRC Centre for Medical Engineering (WT203148/Z/16/Z), and the London AI Centre for Value Based Healthcare via funding from the Office for Life Sciences. BK received funding by the ERC - project MIA-NORMAL 101083647

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The London Dulwich Research Ethics Committee of The Health Research Authority gave ethical approval for this work (reference 22/LO/0163).

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes

Patient-level imaging data from the trial, and the imaging data used to train the AI models are not available for sharing due to ethical restrictions. The study protocol, patient information sheet, and example consent forms are available on request. The code used to train the AI models is available on request, but the model weights used in the trial are not available.