Reducing Diagnostic Errors in Early Breast Cancer Detection Through Radiologist–Generative AI Collaboration Using Multimodal Clinical Data

Abstract

This study has examined the reduction of diagnostic errors in early breast cancer detection through structured collaboration between radiologists and generative artificial intelligence systems operating on multimodal clinical data, within selected United States breast imaging and diagnostic settings. The central problem has been that diagnostic errors in early breast cancer detection, including missed cancers, false positives leading to unnecessary recall and biopsy, and characterization errors, remain a persistent source of patient harm, and that neither unaided radiologist interpretation nor stand-alone artificial intelligence has eliminated them, because unaided reading is subject to perceptual and cognitive limits while stand-alone automation is subject to distributional brittleness, opacity, and loss of clinical context. The purpose of the study has been to evaluate whether multimodal AI integration and data quality, generative AI reasoning and explanation quality, radiologist–AI trust and calibration, workflow and clinical interpretability integration, and governance, safety, and bias mitigation significantly improve diagnostic error avoidance and detection accuracy when radiologist and generative AI operate as a collaborative reading team rather than in isolation. A quantitative, cross-sectional, case-based research design has been used, and data have been collected from breast radiologists, diagnostic radiographers, breast surgeons and oncologists, clinical AI specialists, and biomedical informatics professionals engaged in breast imaging and diagnostic decision-making. Out of 268 distributed questionnaires, 228 valid responses have been retained, producing an 85.1% valid response rate. The analysis plan has included descriptive statistics, Cronbach’s alpha reliability testing, Pearson correlation analysis, multiple regression modeling, hypothesis testing, a prototype reader-study evaluation, and Python-driven analytical workflow validation. The findings have shown strong agreement across the constructs, with multimodal AI integration and data quality recording the highest mean score of 4.26, followed by generative AI reasoning and explanation quality at 4.17, radiologist–AI trust and calibration at 4.08, diagnostic error avoidance and detection accuracy at 4.03, workflow and clinical interpretability integration at 3.97, and governance, safety, and bias mitigation at 3.88. Reliability has been confirmed through Cronbach’s alpha values ranging from .82 to .91, with overall reliability of .93. Correlation results have shown significant positive relationships between diagnostic error avoidance and multimodal AI integration (r = .68), generative AI reasoning (r = .66), radiologist–AI trust (r = .64), workflow integration (r = .60), and governance (r = .58), all at p < .001. The regression model has been significant, F(5, 222) = 52.87, p < .001, explaining 54.3% of the variance. The study implies that collaborative radiologist–generative AI reading on multimodal data can meaningfully reduce diagnostic errors in early breast cancer detection when integration quality, explanation quality, calibrated trust, workflow fit, and governance is jointly present.