Comparison Breast Cancer Radiologists’ Decisions and Trained AI Classification Model: Experimental Study
Vlasov A.V. Jin S. Leonov M.G. Belianin A.V.
Summer 2025Interactive Media Institute
Annual Review of CyberTherapy and Telemedicine
2025#23251 - 257 pp.
This experimental study compares the diagnostic decisions made by radiologists and an artificially intelligent model (EfficientNetB0) in identifying breast cancer via digital mammography. The results demonstrate an overall accuracy of 95.06% for the AI model. Correlation analysis reveals significant differences in agreement levels between radiologists themselves versus their concordance with the AI model, particularly within crucial BIRADS categories (III-V). These findings highlight the need for further investigation into harmonizing expert opinions and AI-based approaches to reduce diagnostic uncertainty and minimize risks for patients at risk of breast cancer.
BIRADS , Breast cancer , EfficientNetB0 , Radiologist decision
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HSE University, Russian Federation
International Lab for Experimental and Behavioural Economics, HSE University, Russian Federation
DDT
Oncologic dispensary No 3 (Novorossiysk), Russian Federation
Kuban State Medical University, Russian Federation
Almaty Management University, Kazakhstan
HSE University
International Lab for Experimental and Behavioural Economics
DDT
Oncologic dispensary No 3 (Novorossiysk)
Kuban State Medical University
Almaty Management University
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026