Reader bias in breast cancer screening related to cancer prevalence and artificial intelligence decision support —a reader study

ConclusionBreast radiologists reading a list with high cancer prevalence performed at considerably higher sensitivity and lower specificity than the original screen-readers. Adding AI information, calibrated to a screening setting, decreased sensitivity and increased specificity.Clinical relevance statementRadiologist screening mammography assessments will be biased towards higher sensitivity and lower specificity by high-risk triaging and nudged towards the sensitivity and specificity setting of AI reads. After AI implementation in clinical practice, there is reason to carefully follow screening metrics to ensure the impact is desired.Key Points• Breast radiologists’ sensitivity and specificity will be affected by changes brought by artificial intelligence.• Reading in a high cancer prevalence setting markedly increased sensitivity and decreased specificity.• Reviewing the binary reads by AI, negative or positive, biased screening radiologists towards the sensitivity and specificity of the AI system.Graphical abstract
Source: European Radiology - Category: Radiology Source Type: research