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Radiological interpretations, while essential, are not infallible and are best understood as expert opinions formed through the evaluation of available evidence. Acknowledging the inherent possibility of error is crucial, as it frames the discussion on improving diagnostic accuracy and patient care. A comprehensive review of error classifications highlights the complexity of diagnostic errors, drawing on recent frameworks to categorize them into perceptual and cognitive errors, among others. This classification underpins an analysis of specific error types, their prevalence, and implications for clinical practice. Additionally, we address the psychological impact of radiological practice, including the effects of mental health and burnout on diagnostic accuracy. The potential of artificial intelligence (AI) in mitigating errors is discussed, alongside ethical and regulatory considerations in its application. This research contributes to the body of knowledge on radiological errors, offering insights into preventive strategies and the integration of AI to enhance diagnostic practices. It underscores the importance of a nuanced understanding of errors in radiology, aiming to foster improvements in patient care and radiological accuracy. [ABSTRACT FROM AUTHOR]

Objectives: This meta-analysis aims to compare the efficacy, limitations, and clinical implications of Abbreviated Breast MRI (AB-MRI) and Full Protocol MRI (FP-MRI). It focuses on diagnostic accuracy across various populations and settings, extending the scope of prior meta-analyses by including studies conducted after 2019 in both screening and diagnostic contexts., Methods: We conducted a systematic review (from 11/2019 to 12/2022). A bivariate model was used to calculate the summary estimates of sensitivity and specificity. Random effect models were used to calculate summary AUC and probability distributions for negative and positive predictive values were obtained. Subgroup analyses were conducted to investigate the differences between the AB-MRI and FP-MRI in terms of sensitivity, specificity, and AUC., Results: The search across multiple databases yielded 11 eligible studies, including one prospective and ten retrospective studies. Statistical analysis revealed a significant difference in sensitivity between FP-MRI (95%) and AB-MRI (86%, p = 0.005), but not in specificity (p = 0.50). AB-MRI demonstrated a shorter acquisition time, suggesting potential for increased patient throughput. However, challenges remain in detecting small lesions and non-mass enhancements, with some studies suggesting the inclusion of additional sequences such as DWI with ADC mapping to enhance diagnostic performance., Conclusion: While FP-MRI remains the gold standard in breast cancer detection, AB-MRI presents as a viable, quicker alternative, especially useful in high-risk screening scenarios. Its lower sensitivity compared to FP-MRI, however, limits its utility as a standalone diagnostic tool. Future research should focus on optimizing AB-MRI protocols and investigating patient-specific factors to refine breast cancer screening and diagnostic strategies., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Institute of Radiology.)