This study aims to evaluate the diagnostic performance and clinical utility of a multimodal medical imaging large model in identifying common systemic diseases. Through a retrospective reader study involving multiple centers, the research will compare the diagnostic accuracy, sensitivity, and specificity of radiologists with and without AI assistance. The goal is to validate the model's robustness and its impact on the diagnostic efficiency of clinicians across diverse healthcare settings.
Patients who underwent systemic medical imaging examinations (e.g., CT or MRI) at participating centers for common systemic diseases.
Imaging data must have confirmed clinical reference standards, expert consensus, or pathological diagnosis.
Availability of complete DICOM format images with standard acquisition protocols.
Poor image quality (e.g., severe motion or metal artifacts) that precludes definitive diagnosis.
Cases with incomplete clinical or pathological reference standards.
Corrupted image files or duplicate cases.