A 12-step interpretive-reading scaffold for challenging and rare-disease radiology: educational perspective based on 47 image-pathology components

Radiologists are vulnerable to "common-disease-first" cognitive bias when interpreting challenging and rare-disease cases, which may lead to misdiagnosis and missed diagnosis1,2,3. This article proposes a streamlined structured 12-step interpretive-reading scaffold built upon a 47-component image-pathology decomposition toolkit to structure the reasoning chain for rare-disease imaging. This is a purely theoretical educational manuscript without any real-patient datasets. Six representative cross-system rare diseases are adopted for simulated theoretical demonstrations to illustrate the practical logic of this workflow. Advantages and explicit limitations are discussed, and further validation by multicentre clinical cohorts is required.