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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]

Breast cancer is the most common forms of cancer and a leading cause of mortality in women. Early and correct diagnosis is, therefore, essential to save lives. The development of diagnostic imaging applied to the breast has been impressive in recent years and the most used diagnostic test in the world is mammography, a low-dose X-ray technique used for imaging the breast. In the first half of the 20th century, the diagnosis was in practice only clinical, with consequent diagnostic delay and an unfavorable prognosis in the short term. The rise of organized mammography screening has led to a remarkable reduction in mortality through the early detection of breast malignancies. This historical review aims to offer a complete panorama of the development of mammography and breast imaging during the last century. Through this study, we want to understand the foundations of the pillar of radiology applied to the breast through to the most modern applications such as contrast-enhanced mammography (CEM), artificial intelligence, and radiomics. Understanding the history of the development of diagnostic imaging applied to the breast can help us understand how to better direct our efforts toward an increasingly personalized and effective diagnostic approach. The ultimate goal of imaging applied to the detection of breast malignancies should be to reduce mortality from this type of disease as much as possible. With this paper, we want to provide detailed documentation of the main steps in the evolution of breast imaging for the diagnosis of breast neoplasms; we also want to open up new scenarios where the possible current and future applications of imaging are aimed at being more precise and personalized. [ABSTRACT FROM AUTHOR]

Recent technological advances in the field of artificial intelligence hold promise in addressing medical challenges in breast cancer care, such as early diagnosis, cancer subtype determination and molecular profiling, prediction of lymph node metastases, and prognostication of treatment response and probability of recurrence. Radiomics is a quantitative approach to medical imaging, which aims to enhance the existing data available to clinicians by means of advanced mathematical analysis using artificial intelligence. Various published studies from different fields in imaging have highlighted the potential of radiomics to enhance clinical decision making. In this review, we describe the evolution of AI in breast imaging and its frontiers, focusing on handcrafted and deep learning radiomics. We present a typical workflow of a radiomics analysis and a practical "how-to" guide. Finally, we summarize the methodology and implementation of radiomics in breast cancer, based on the most recent scientific literature to help researchers and clinicians gain fundamental knowledge of this emerging technology. Alongside this, we discuss the current limitations of radiomics and challenges of integration into clinical practice with conceptual consistency, data curation, technical reproducibility, adequate accuracy, and clinical translation. The incorporation of radiomics with clinical, histopathological, and genomic information will enable physicians to move forward to a higher level of personalized management of patients with breast cancer. [ABSTRACT FROM AUTHOR]