Radiology braces for deep AI productivity gains by 2030

However, adoption varies sharply across the imaging workflow.

Artificial intelligence (AI) tools are moving deeper into radiology workflows, with a projected 20% to 30% radiologist productivity savings by 2030 and up to 50% in the longer term.

L.E.K. Consulting said the gains would come mainly from routing and image interpretation, reporting and quality assurance, and communication and admin.

However, the AI imaging software market itself is fragmented but expanding fast. The number of CE-certified AI radiology products in Europe rose from 100 in 2020 to 173 in 2023 — a 20% annual growth rate.

Adoption varies sharply across the imaging workflow, as voice recognition and reporting tools show the highest uptake, with report-assist AI delivering a 30% faster turnaround for radiologists.

Intelligent triage and worklist tools are also widely used across large networks and teleradiology operations. By contrast, diagnostic decision-support AI in image interpretation sees mixed use, with radiologists citing oversensitivity and false positives as partial offsets to speed gains.

Reporting and QA show the largest near-term shift—AI-assisted structured reporting is projected to cut the time radiologists spend on that task from 36% to 18% of the workflow stage.

This builds on productivity gains already underway. In Australia specifically, the National Lung Cancer Screening Program saw Medicare-funded services nearly double to 8,787 in August from 4,660 in July 2026.

The report noted that workforce pressures may also be easing on the supply side. Overseas-trained radiodiagnosis fellows entering Australia averaged 34 a year in 2021 to 2022, up from just 6 a year in the prior four years.

Despite this, L.E.K. concludes radiologists will remain central to the field. Scanning, patient safety and clinical accountability for incidental findings will continue to require human oversight, the report notes, even as radiology evolves into a more multimodal discipline incorporating pathology and genomic data.