AI in radiology and interventions: a structured narrative review of workflow automation, accuracy, and efficiency gains of today and what’s coming

Abstract

PURPOSE: Artificial intelligence (AI) is rapidly transforming diagnostic and interventional radiology, supported by accelerating regulatory approvals and clinical adoption. Despite progress, integration varies across modalities and procedures. This study is a structured narrative review of four representative workflows-MRI and CT screening, coronary stenting, and liver cryoablation-to quantify automation readiness, accuracy gains, and efficiency improvements. The novelty lies in comparing diagnostic and interventional domains to highlight distinct maturity levels and future opportunities for AI-driven workflow optimization and clinical value creation. METHODS: A structured analysis was performed identifying 43 workflow steps across the four selected procedures. Each step was evaluated for potential automation, accuracy improvement, and ability to provide new clinical insights, considering current availability and projected 2030 maturity. The assessment drew on peer-reviewed literature, FDA approvals, and industry data (2015-2025). A structured taxonomy distinguished between full automation, human-augmented improvements, and novel AI-enabled guidance functions. RESULTS: Diagnostic imaging showed higher maturity than interventional workflows. Currently, 70% of MRI and 64% of CT steps have available AI solutions, compared to 55% in coronary stenting and 36% in liver cryoablation. By 2030, nearly all steps are expected to be AI-supported. AI achieved up to 94% segmentation accuracy, 95% nodule detection sensitivity, 30-75% scan time reductions, and 30-50% faster reporting. Interventional applications improved catheter navigation, probe placement, and ablation success but still required significant human oversight. CONCLUSIONS: AI has already demonstrated measurable gains in diagnostic accuracy, efficiency, and workflow standardization. Interventional applications are emerging, with future growth expected in guidance, robotics, and real-time optimization. Despite progress, key limitations include algorithm generalizability, clinical interpretability, organizational readiness, and regulatory uncertainty. AI will augment rather than replace human expertise, with collaborative human-AI workflows being essential. Future integration efforts must address interoperability, workforce adaptation, and ethical considerations to ensure safe, equitable, and clinically impactful deployment.

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