"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To evaluate a Sham-AI model acting as a placebo control for a Standard-AI model for intracranial aneurysm diagnosis. Materials and Methods This retrospective crossover, blinded, multireader multicase study was conducted from November 2022 to March 2023. A Sham-AI model with near-zero sensitivity and similar specificity to a Standard-AI model was developed using 16,422 CT angiography (CTA) examinations. Digital subtraction angiography-verified CTA examinations from four hospitals were collected, half of which were processed by Standard-AI and the others by Sham-AI to generate Sequence A; Sequence B was generated reversely. Twenty-eight radiologists from seven hospitals were randomly assigned with either sequence, and then assigned with the other sequence after a washout period. The diagnostic performances of radiologists alone, radiologists with Standard-AI-assisted, and radiologists with Sham-AI-assisted were compared using sensitivity and specificity, and radiologists' susceptibility to Sham-AI suggestions was assessed. Results The testing dataset included 300 patients (median age, 61 (IQR, 52.0-67.0) years; 199 male), 50 of which had aneurysms. Standard-AI and Sham-AI performed as expected (sensitivity: 96.0% versus 0.0%, specificity: 82.0% versus 76.0%). The differences in sensitivity and specificity between Standard-AI-assisted and Sham-AIassisted readings were +20.7% (95%CI: 15.8%-25.5%, superiority) and 0.0% (95%CI: -2.0%-2.0%, noninferiority), respectively. The difference between Sham-AI-assisted readings and radiologists alone was-2.6% (95%CI: -3.8%--1.4%, noninferiority) for both sensitivity and specificity. 5.3% (44/823) of true-positive and 1.2% (7/577) of false-negative results of radiologists alone were changed following Sham-AI suggestions. Conclusion Radiologists' diagnostic performance was not compromised when aided by the proposed Sham-AI model compared with their unassisted performance. Published under a CC BY 4.0 license.