Introduction Computer vision is a clinically deployed component of diagnostic radiology, supporting detection, classification/triage, and segmentation/quantification. International evidence is substantial but concentrated in high-resource systems, with a gap between technical accuracy and clinical utility. Poland launched a nationally coordinated diagnostic artificial intelligence (AI) rollout in 2025–2026 but is under-represented in this literature. This review summarises computer vision performance across main diagnostic tasks and describes Poland’s implementation context, centred on the Platforma Usług Inteligentnych (PUI). Methods A narrative review, reported in line with SANRA criteria, of PubMed/MEDLINE, Google Scholar, and Polish institutional and grey-literature sources published 2022–2026. Results Detection, classification/triage, and segmentation/quantification are best validated in chest, neurological, and breast imaging, largely overlapping with PUI’s five prioritised domains (chest computed tomography (CT), chest radiograph, mammography, brain CT/magnetic resonance imaging, skeletal radiograph). Clinical utility and outcome evidence remains scarcer than accuracy data. PUI is funded through the EU-backed Krajowy Plan Odbudowy; vendors were selected in all five model areas, but routine production use after the 30 June 2026 deadline was unconfirmed (as of 19 October 2026). PUI responds to workload growth and regional and subspecialty distribution among fewer than 4,600 radiologists (about 123 per million; EU average 127), not to a per-capita deficit. In a national survey, 4.7% of institutions used AI, 33.9% of them in CT. Published Polish evidence on diagnostic AI remains limited. Conclusions PUI enters a technically well-evidenced field but is a live test of the under-studied validation gap, offering Polish radiology a substantial opportunity to contribute original evidence.
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Praveen Kumar Malik (2026) studied this question.