Clinical Scorecard: The Role of Artificial Intelligence in Radiology: A Comprehensive Review of Current Workflow Automation, Diagnostic Accuracy, and Future Efficiency Enhancements

At a Glance

Key Highlights

• Over 1,247 AI-enabled medical devices authorized by FDA as of August 2025, with radiology comprising >75% of approvals
• AI applications span automation (e.g., DL reconstruction), improvement (e.g., CAD triage), and new insights (e.g., wire-free physiology, opportunistic biomarkers)
• Human-in-the-loop model emphasized with regulatory frameworks mandating oversight, data governance, bias monitoring, and post-market surveillance

Guideline-Based Recommendations

Diagnosis

• Utilize AI to augment diagnostic accuracy in imaging modalities such as MRI and CT with validated deep learning algorithms
• Apply AI-based lesion detection and characterization with demonstrated accuracy (e.g., 87.6% in MRI cancer screening)
• Ensure external validation and bias assessment of AI tools prior to clinical deployment

Management

• Adopt AI-enabled workflow automation where residual clinical risk is low and outputs are directly actionable under human supervision
• Use AI to improve speed, accuracy, and consistency of clinician-led steps without replacing clinical judgment
• Integrate AI-generated new insights cautiously, requiring further validation before automation

Monitoring & Follow-up

• Implement continuous post-market monitoring and change control for AI systems to detect performance drift and maintain safety
• Maintain transparency and interoperability standards in AI deployment
• Conduct lifecycle monitoring aligned with risk-based regulatory frameworks

Risks

• Recognize differing error profiles between AI and clinicians necessitating collaborative human-in-the-loop models
• Avoid full automation in high-stake management decisions under uncertainty
• Address potential bias and variability across sites and patient subgroups

Patient & Prescribing Data

Patients undergoing diagnostic imaging and interventional procedures in oncology, cardiology, and hepatology

AI-enabled devices improve diagnostic accuracy and workflow efficiency but require human oversight and prospective real-world validation to ensure clinical impact

Clinical Best Practices

• Employ AI tools with demonstrated regulatory approval and clinical validation in the relevant imaging workflow steps
• Maintain human oversight in all AI-assisted procedures to mitigate risks and ensure patient safety
• Use risk-based frameworks to guide AI integration, emphasizing data governance, transparency, and bias monitoring
• Continuously monitor AI system performance post-deployment to detect drift and maintain efficacy
• Adopt a collaborative approach combining AI strengths with clinician expertise for optimal diagnostic and therapeutic outcomes

References

• FDA AI-enabled Medical Devices Authorizations
• RSNA AI Resources
• Deep Learning in MRI Reconstruction
• AI in Radiology Workflow Automation