AI in Radiology: Transforming Imaging Practices and Clinical Workflows

Radiology is undergoing rapid change as artificial intelligence is reshaping image interpretation and operational workflows across specialties.

Artificial intelligence (AI) applications in breast imaging are particularly noteworthy. AI models applied to contrast-enhanced mammography (CEM) images enhance diagnostic accuracy by optimizing lesion detection and reducing false positives. Studies report performance metrics for AI-assisted detection in breast imaging, such as area under the ROC curve and sensitivity/specificity, with details available in the linked study on AI in breast imaging. Professional society guidance further details AI integration, underscoring best practices that can influence mammography workflows and outcomes. These considerations are critical to ensuring that AI-enabled follow-ups aid early intervention and patient management without overstating capability.

Beyond the breast domain, respiratory imaging illustrates how quantitative methods are reshaping disease assessment. AI's role in respiratory diagnostics is pivotal, particularly for bronchiectasis assessment, including in COPD cohorts, where airway-artery ratio quantification supports appraisal of airway dilation and disease burden. This approach offers deeper insight into patterns of progression that can inform multidisciplinary discussions in thoracic imaging research.

Within this context, some AI models quantify bronchial and adjacent vascular metrics, supporting clinical assessment and potentially informing interventions. Taken together, these developments emphasize the importance of pairing algorithmic quantification with clinical context, including symptoms, exacerbation history, and pulmonary function testing, to avoid overinterpreting imaging biomarkers.

Timely head CT referrals in emergencies pose additional challenges under pressure. The ACCEPT-AI initiative outlines a framework to evaluate AI in acute imaging, highlighting potential to streamline triage processes in its protocol. For example, AI systems can flag suspected intracranial hemorrhage on head CT for expedited radiologist review, creating a practical bridge from framework to bedside use. Together, these tools aim to augment—not replace—clinician judgment.

As health systems evaluate deployment, evidence quality remains central. Many studies are retrospective, single-center, or use enriched datasets, which can inflate apparent performance. External validation across institutions, prospective impact studies, and clear reporting of intended use are prerequisites for safe adoption. Calibration, failure modes, and generalizability should be scrutinized before integrating tools into routine care.

Operational considerations also matter. Integration with PACS/RIS, alert fatigue management, and monitoring for drift require collaboration between radiology, IT, and quality teams. Defining success metrics—such as time-to-report for critical findings, recall rates in screening programs, or reduced unnecessary imaging—helps align tools with clinical priorities while guarding against unintended consequences.

Ethics and governance are equally important. Transparent documentation of training data, attention to demographic representativeness, and adherence to privacy and security standards underpin trustworthy AI. Human-in-the-loop review remains essential, with pathways for rapid override when algorithms misfire. Clear communication with patients about AI assistance can bolster trust without implying autonomy.

Education and workforce implications continue to evolve. Radiologists, technologists, and trainees benefit from curricula that cover algorithmic principles, performance interpretation, and practical troubleshooting. Familiarity with uncertainty communication and bias mitigation equips teams to deploy AI responsibly and to recognize when model outputs should be questioned.

Looking ahead, cross-specialty learning will accelerate progress. Methods refined in breast or thoracic imaging—such as uncertainty estimation, case prioritization, or structured reporting—can inform neuro and musculoskeletal applications. Conversely, advances in neurocritical triage may loop back to improve workflow orchestration in other service lines, creating a virtuous cycle of shared best practices.

Key Takeaways:

• Breast imaging AI is maturing, with reported AUC and sensitivity/specificity metrics available in peer-reviewed studies, and its integration should follow professional guidance to support early detection without overstating capability.
• In thoracic imaging, quantitative AI for bronchiectasis—including airway-artery ratio analysis—can support assessment of airway dilation and disease burden, while claims about outcome improvements should remain cautious.
• In emergency neuroimaging, frameworks such as ACCEPT-AI describe how to evaluate AI's role in triage; practical examples include systems that flag suspected hemorrhage for expedited review.
• Robust validation, governance, and operational planning are essential to translate algorithmic promise into reliable clinical value across specialties.