Radiological interpretations, though vital, are not without limitations and should be viewed as expert judgments based on the available evidence. Recognizing the potential for error is essential, as it sets the foundation for efforts to enhance diagnostic accuracy and improve patient outcomes. A detailed examination of error classifications reveals the multifaceted nature of diagnostic mistakes, utilizing recent frameworks to distinguish between perceptual and cognitive errors, among others. This classification supports a deeper analysis of specific error types, their frequency, and their impact on clinical practice. The discussion also explores the psychological challenges faced by radiologists, including how mental health and burnout can influence diagnostic performance. Furthermore, the role of artificial intelligence (AI) in reducing errors is examined, with attention given to its ethical and regulatory implications. This study adds to the growing body of knowledge on radiological errors, offering perspectives on prevention strategies and the integration of AI to strengthen diagnostic processes. Ultimately, it highlights the need for a nuanced understanding of diagnostic errors to support improvements in radiological accuracy and patient care.

Keywords: diagnostic; errors; radiology; methodology; AI