Objective 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. Materials and methods 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. Results 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. Conclusion 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. Key Points <Emphasis Type="BoldItalic">Question</Emphasis> Is there any high-quality evidence from randomized controlled trials (RCTs) to evaluate the clinical benefits of artificial intelligence (AI) in radiological image diagnosis? <Emphasis Type="BoldItalic">Findings</Emphasis> 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. <Emphasis Type="BoldItalic">Clinical relevance</Emphasis> 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. Graphical Abstract

Efficacy evaluation of artificial intelligence in radiological imaging diagnosis based on randomized controlled trials: a scoping review

期刊论文
发表日期：

2026年7月6日

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Yiqiao Yan,
Chuan Liu,
Hang Fu,
Ke Xu,
Huayan Xu

摘要

Objective

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.

Materials and methods

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.

Results

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.

Conclusion

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.

Key Points

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.

Graphical Abstract