Efficacy evaluation of artificial intelligence in radiological imaging diagnosis based on randomized controlled trials: a scoping review.
Authors
Affiliations (3)
Affiliations (3)
- Department of Radiology, Medical Imaging Center, West China Second University Hospital, Sichuan University, Chengdu, China.
- Department of Radiology, Medical Imaging Center, West China Second University Hospital, Sichuan University, Chengdu, China. [email protected].
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Development and Related Diseases of Women and Children Key Laboratory of Sichuan Province, Children's Medicine Key Laboratory of Sichuan Province, West China Second University Hospital, Sichuan University, Chengdu, China. [email protected].
Abstract
Artificial intelligence (AI) demonstrates significant potential in medical imaging diagnosis, yet its real-world clinical value requires validation through high-quality randomized controlled trials (RCTs). Existing RCTs report heterogeneous results across settings and outcomes, motivating a scoping review to map current evidence and identify gaps. This scoping review mapped RCTs published up to March 2026 that evaluated AI tools for imaging-based diagnosis in radiology in clinical settings. We systematically searched PubMed, Embase, and Web of Science, screened studies using predefined eligibility criteria, and extracted study characteristics and outcomes. Risk of bias was assessed using QUADAS-2 and the RoB 2 tool, and findings were synthesized descriptively in line with PRISMA-ScR. By analyzing the included RCTs, AI tools for imaging-based diagnosis in radiology were mainly deployed as clinician-facing decision aids and were generally associated with higher sensitivity or lesion detection rates and shorter image-processing time. However, the benefits were smaller in complex scenarios such as emergency care, and low specificity remained a common limitation. Overall, AI tools for imaging-based diagnosis in radiology are currently used mainly as clinician-facing decision aids and may be most beneficial in standardized tasks, with effects varying across clinical settings. Larger multicenter prospective RCTs with consistent, clinically meaningful endpoints are needed to support robust clinical translation. Question Is there any high-quality evidence from randomized controlled trials (RCTs) to evaluate the clinical benefits of artificial intelligence (AI) in radiological image diagnosis? Findings Through a review of nine RCTs, AI tools for imaging-based diagnosis tended to raise sensitivity, but gains were smaller in emergency care and specificity often remained low. Clinical relevance As an assistive tool, AI can improve imaging sensitivity and reduce missed diagnoses in standardized scenarios; however, further high-quality RCT evidence is still needed.