This Arkangel AI whitepaper is a rapid narrative evidence brief that synthesizes 27 sources on five selected radiology tasks: assisted mammography reading, detection of specific chest radiograph findings, prioritization and efficiency, segmentation or quantification, and text support for reports. The main search ran on June 13, 2026, with a reproducible PubMed retrieval in July that returned 2,337 unique records for mapping and source verification in September. Selection was neither exhaustive nor done by two independent reviewers, and each task was rated across six evidence levels, from retrospective discrimination to routine implementation.
Within this corpus, the most developed prospective evidence was for mammography reading support in organized screening programs, notably the Swedish MASAI trial. In a single-center randomized trial of 10,476 health-checkup participants, radiologists reading chest radiographs with AI support found CT-confirmed actionable nodules in 0.59% of participants versus 0.25% without it. Triage showed heterogeneous effects on reading times and workload, segmentation lacked a general level of clinical maturity, and language-model report support still needs human oversight because of errors. Regional sources, including one Mexican retrospective study with an AUC of 0.774 for COVID-19 on CT, do not show generalizable effectiveness in Latin America. It is a whitepaper, not peer reviewed, and the authors are affiliated with Arkangel AI.
