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Artificial-intelligence systems are increasingly being evaluated for automated fetal biometry and standard-plane recognition in obstetric ultrasound. Research studies show that deep-learning models can assist with image classification and measurement, potentially reducing operator variability and improving workflow. However, performance can change with different ultrasound machines, operators and patient populations. For clinicians, the near-term role of AI is therefore best viewed as decision support rather than autonomous diagnosis. Automated outputs should be checked against the clinical picture, gestational age and image quality. Prospective multicenter validation remains important before widespread autonomous deployment.
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Artificial-intelligence systems are increasingly being evaluated for automated fetal biometry and standard-plane recognition in obstetric ultrasound. Research studies show that deep-learning models can assist with image classification and measurement, potentially reducing operator variability and improving workflow. However, performance can change with different ultrasound machines, operators and patient populations. For clinicians, the near-term role of AI is therefore best viewed as decision support rather than autonomous diagnosis. Automated outputs should be checked against the clinical picture, gestational age and image quality. Prospective multicenter validation remains important before widespread autonomous deployment.
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