Quantifying Human–AI Workflow in Abdominal Ultrasound: A Prospective Randomised Crossover Study
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Objective
To evaluate the effect of vendor-integrated AI-assisted abdominal ultrasound software on operational efficiency and sonographer workload compared with manual scanning.
Methods
In this prospective randomised crossover study (January to February 2026), 32 healthy adults each underwent two upper abdominal examinations, one manual and one using vendor-integrated AI software (AI Abdomen Release 3.5; ACUSON Sequoia), in randomised order by two experienced sonographers; each participant was scanned once by each sonographer. Scan time, hand–console interaction (keystrokes, hand travel, hover, jerk) from a custom depth-camera hand-tracking system, and operator modifications to AI outputs were recorded. Workload was assessed after each scan with the weighted NASA Task Load Index (NASA-TLX). Analysis used linear mixed-effects models.
Results
AI-assisted scanning reduced scan time (52.4 s, approximately 9%; 95% CI 23.7 to 81.2; P = 0.001), keystrokes (55, approximately 28%; P < 0.001) and hand travel (4.57 m, approximately 39%; P < 0.001), although the time saving was concentrated in one sonographer. Weighted NASA-TLX did not differ between conditions (−3.9 points; 95% CI −9.3 to 1.5; P = 0.17), but subscale analyses showed reductions in mental demand (−6.3; P = 0.03) and effort (−7.0; P = 0.04), with no compensating increases. Sonographers modified 48 of 184 AI-generated values.
Conclusion
AI assistance improved operational efficiency and reduced self-reported mental demand and effort, with no compensating increase on other subscales. Gains arose under a controlled, abbreviated protocol in healthy volunteers and varied between operators, and are better read as a reshaping of operator work than its removal.
