Mitigating errors in musculoskeletal radiology: a multilevel framework

Reporting errors are an unavoidable reality for any active, practicing radiologist. However, they can be minimised through open discussion, meticulous training, and openness to feedback. In an era marked by escalating imaging volumes, complex caseloads, and systemic workforce burnout, achieving an error-free practice is a statistical impossibility; baseline radiological error rates are estimated in the literature to range between 3% and 5% for decades. However, this reality should not deter radiologists from pursuing continuous diagnostic refinement. This article explores the intricate cognitive and perceptual pitfalls that lead to interpretive misses in musculoskeletal (MSK) radiology reporting, such as satisfaction of search, anchoring bias, framing bias, and the misinterpretation of common anatomical variants or technical artefacts (like the magic angle effect). Utilising illustrated case examples to highlight these common blind spots, the review transitions from identifying diagnostic errors to outlining actionable, multi-tiered strategies for mitigation. At the individual level, it emphasises checklist utility and historical image comparison. At the departmental level, it advocates for a shift from blame-free frameworks toward a 'just culture' via Radiology Events and Learning Meetings (REALM), while directly addressing the detrimental impact of medical incivility on psychological safety. Finally, at the international level, it examines the integration of artificial intelligence as a collaborative second reader. This review helps radiologists transform interpretive errors into shared clinical knowledge, with the aim of reducing or minimising errors and ultimately promoting patient safety in a psychologically safe environment.