The study included 3,495 transthoracic echocardiograms performed on adults for any clinical indication. The proportion of studies substantially changed was 16.8% in the AI group and 27.2% in the sonographer group (difference -10.4%, 95% confidence interval [CI] -13.2% to -7.7%, p<0.001 for noninferiority, p<0.001 for superiority). The safety endpoint was the difference between the final cardiologist report and a historical cardiologist report. The mean absolute difference was 6.29% in the AI group and 7.23% in the sonographer group (difference -0.96%, 95% CI -1.34% to -0.54%, p<0.001 for superiority).

Dr. Ouyang said: “We learned a lot from running a randomised trial of an AI algorithm, which hasn’t been done before in cardiology. First, we learned that this type of trial is highly feasible in the right setting, where the AI algorithm can be integrated into the usual clinical workflow in a blinded fashion. Second, we learned that blinding really can work well in this situation. We asked our cardiologist over-readers to guess if they thought the tracing they had just reviewed was performed by AI or by a sonographer, and it turns out that they couldn’t tell the difference – which both speaks to the strong performance of the AI algorithm as well as the seamless integration into clinical software. We believe these are all good signs for future trial research in the field.”


He concluded: “We are excited by the implications of the trial. What this means for the future is that certain AI algorithms, if developed and integrated in the right way, could be very effective at not only improving the quality of echo reading output but also increasing efficiencies in time and effort spent by sonographers and cardiologists by simplifying otherwise tedious but important tasks. Embedding AI into clinical workflows could potentially provide more precise and consistent evaluations, thereby enabling earlier detection of clinical deterioration or response to treatment.”

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