Autonomous chest x-ray image classification, capabilities and prospects: rapid evidence assessment
Modern AI systems for autonomous triage of CXRs are ready to be implemented in clinical practice. AI-driven screening can reduce radiologists' workload, decrease sorting errors and lower the costs associated with screening programs. However, implementation is often hindered by regulatory and legislative barriers. Consequently, comprehensive clinical trials conducted under real-world conditions remain scarce.
Authors
- Olga V. Omelyanskaya (ORCID: https://orcid.org/0000-0002-0245-4431)
- Anton V. Vladzymyrskyy (ORCID: https://orcid.org/0000-0002-2990-7736)
- Alexander V. Bazhin (ORCID: https://orcid.org/0000-0003-3198-1334)
- Pavel Gelezhe (ORCID: https://orcid.org/0000-0003-1072-2202)
- Roman V. Reshetnikov (ORCID: https://orcid.org/0000-0002-9661-0254)
- Olga Nanova (ORCID: https://orcid.org/0000-0001-8886-3684)
- Yuriy А. Vasilev (ORCID: https://orcid.org/0000-0002-5283-5961)
- Kirill Arzamasov
Institutions
- Sechenov University (RU)
- National Medical and Surgical Center named after N.I. Pirogov (RU)
- MIREA - Russian Technological University (RU)
- Medical Technologies (Czechia) (CZ)
Publication Details
- Journal
- Frontiers in Digital Health
- Published
- 2026-01-13
- DOI
- https://doi.org/10.3389/fdgth.2025.1685771
- Primary Topic
- COVID-19 diagnosis using AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00