Incorrect ADs reduce the interpreter's diagnostic accuracy indicating an automation bias. Non-CFs tend to agree more with the ADs in comparison to CFs, hence less expert physicians are more effected by automation bias. Incorrect ADs reduce the interpreter's confidence and also reduces the predictive power of confidence for predicting accuracy (even more so for non-CFs). Whilst a statistically significant model was developed, it is difficult to predict interpretation accuracy using machine learning on basic features such as interpreter confidence, age, reader experience and designation.

No abstract

Diagnostic proposals affect the diagnostic accuracy of ECG interpretations. The accuracy is significantly influenced especially when a single diagnostic proposal (either correct or incorrect) is provided. The study suggests that the presentation of multiple computerized diagnoses is likely to improve the diagnostic accuracy of interpreters.