Purpose
This systematic review aimed to evaluate electrocardiogram interpretation competency among emergency and critical care nurses and to examine the diagnostic performance, benefits, and limitations of computerized and artificial intelligence–based electrocardiogram interpretation systems.
Methods
This systematic review was conducted in accordance with PRISMA 2020 guidelines and registered in the International Prospective Register of Systematic Reviews under registration number CRD420251169307. Six electronic databases and additional sources were searched for studies published between January 2020 and October 2025, with the final search conducted in October 2025. Studies were included if they involved registered nurses interpreting electrocardiograms in acute care settings or evaluated computerized electrocardiogram interpretation systems using adult datasets. Methodological quality was assessed using validated tools appropriate to study design, including the Joanna Briggs Institute critical appraisal tools, ROBINS-I, and QUADAS-2.
Results
Mean electrocardiogram interpretation scores among nurses ranged from 43% to 68%, with fewer than 40% of participants meeting predefined competency thresholds. Performance was strongest for asystole recognition and weakest for tachyarrhythmias, myocardial ischemia, and conduction abnormalities. Artificial intelligence–based systems demonstrated high diagnostic accuracy, with area under the curve values ranging from 0.91 to 0.97 and sensitivity exceeding 94% across major diagnostic tasks.
Conclusion
Emergency and critical care nurses demonstrated insufficient electrocardiogram interpretation competency in several safety-critical domains. Computerized and artificial intelligence–based systems showed high diagnostic accuracy and may serve as effective complementary tools when integrated with ongoing nurse education and appropriate clinical oversight.