Autonomous artificial intelligence for sorting results of preventive radiological examinations of chest organs: medical and economic efficiency

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BACKGROUND: This article proposes a model for organizing preventive radiological examinations of chest organs through autonomous sorting of examination results using medical devices based on artificial intelligence technologies, optimized for maximum sensitivity — 1.0 (95% CI: 1.0; 1.0). Sorting involves classifying the results of mass preventive screenings (fluoroscopy and chest X-rays) into two: “not normal” and “normal.” The “not normal” category includes all cases of abnormalities (e.g., pathological conditions, post-disease or post-surgery consequences, and age-related and congenital features), which are sent for interpretation by a radiologist. The “normal” category consists of cases without signs of pathological deviations, which potentially do not require a radiologist’s description. AIM: To evaluate the feasibility, effectiveness, and efficiency of autonomous sorting of results from preventive radiological examinations of chest organs. MATERIALS AND METHODS: A prospective multicenter diagnostic study was conducted on the safety and quality of autonomous sorting of results from preventive radiological examinations of chest organs. Analytical and statistical methods of scientific inquiry were used. RESULTS: The study included results from 575,549 preventive radiological examinations obtained through fluoroscopy and chest X-rays and processed using three medical devices based on artificial intelligence technologies. In autonomous sorting, 54.8% of the preventive radiological examinations of chest organs were classified as “normal,” resulting in a proportional reduction in the radiologist’s workload for interpreting and describing the examination results. Fully correct autonomous sorting was achieved in 99.95% of cases. Clinically significant discrepancies were determined in 0.05% of cases (95% CI: 0.04; 0.06%). CONCLUSIONS: This study confirmed the medical and economic effectiveness of the model for autonomous sorting of results from preventive radiological examinations of chest organs using medical devices based on artificial intelligence technologies. The next phase should involve updating the regulatory framework and ensuring the legitimacy of the autonomous application of certain medical devices based on artificial intelligence technologies in established conditions and preventive tasks.