Variability in Decision Making of Breast Cancer Radiologists and Commercial AI Models: Experimental Study
Vlasov A.V. Jin S. Leonov M.G. Belianin A.V.
Summer 2025Interactive Media Institute
Annual Review of CyberTherapy and Telemedicine
2025#23116 - 121 pp.
Breast cancer represents a major challenge in womens healthcare globally, underscoring the need for precise and dependable diagnostic techniques. Although substantial progress has been achieved in imaging technologies and physician training, significant variation exists in how radiologists interpret mammographic images. This study investigates discrepancies between diagnoses made by experienced radiologists and those generated by commercial artificial intelligence platforms. Through an experimental framework, we contrast outputs from AI systems against diagnoses rendered by skilled radiologists, paying particular attention to cases affected by cognitive biases. Our results indicate strong correlations between radiologists’ decisions and AI outputs, raising few concerns about AI reliability. However, they highlight the necessity of careful implementation and monitoring when incorporating AI into clinical workflows, ensuring compliance with established diagnostic norms, and principles of informed risk aversion.
BIRADS , Breast cancer , Commercial AI models , informed risk aversion for radiologists , Radiologist decision
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HSE University, Russian Federation
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Kyrgyz State Medical Academy, Kyrgyzstan
DDT
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Almaty Management University, Kazakhstan
HSE University
International Lab for Experimental and Behavioural Economics
Kyrgyz State Medical Academy
DDT
Oncologic dispensary No 3
Kuban State Medical University
Almaty Management University
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