Artificial Intelligence in Medical Imaging: Clinical Translation, Validation, and Responsible Deployment

Abstract

Background: The adoption of medical imaging in radiology, pathology, and image-based screening creates opportunities to improve clinical services, but technical performance alone does not establish patient benefit. Objective: To critically examine applications, evidence, safety, equity, and implementation of medical imaging. Approach: This narrative review develops an analytical synthesis of established clinical AI principles and reporting guidance; it does not report a systematic search, a new dataset, or an empirical trial. Findings: Key opportunities include lesion detection, triage, segmentation, and report support. Important limitations include scanner variability, spectrum effects, and domain shift, as well as false positives, missed findings, and incidental abnormalities. Responsible adoption requires human review of ambiguous findings and critical results, transparent reporting, and evaluation of diagnostic accuracy, calibration, workload, and downstream procedures. Conclusion: The clinical value of medical imaging depends on fit with the care pathway, prospective evaluation, accountable human oversight, and sustained monitoring after deployment.