The rationale for use of artificial intelligence (AI) in radiology departments to analyze medical images in real-life clinical practice was studied in a multicenter prospective trial. This was a part of the “Experiment on the use of innovative technologies in the field of computer vision for the analysis of medical images and further use in the healthcare system of Moscow” taking place in 2020. The trial included 18 different AI systems and 538 participating radiologists, all working within Unified Radiological Information Service. We evaluated applicability of AI systems, demand from radiologists, the quality of AI implementation, radiologists adaptability and AI impact on the overall radiologists productivity. The final analysis included 1 762 949 AI processing results and 15 028 feedbacks from radiologists. Commitment of radiologists to use AI systems was 22.4%. Also 65% of the tested AI systems didn’t increase maximal timeline set for the image analysis. AI implementation for analyzing prophylactic mammography images accelerated delivery of the results in outpatient and inpatient setting by 15.0% (p=0.03) and 50.0% (p=0.05) respectively. Lung CT and low-dose CT image analysis (searching for potential lung cancer) took radiologists longer to perform by 42.0% of their standard time (p=0.04) when using AI systems. Such contradictory results of AI implementation in different radiology sub-specialties need to be further analyzed. Overall the study results suggest time-saving rationale for using AI systems in radiology departments, including emergency settings. The output of AI image analysis should be verified by radiologist.
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3. Kakileti ST, Madhu HJ, Krishnan L, et al. Observational Study to Evaluate the Clinical Efficacy of Thermalytix for Detecting Breast Cancer in Symptomatic and Asymptomatic Women. JCO Glob Oncol. 2020 ; 6: 1472-1480. doi : 10.1200/GO.20.00168.
4. Methodology for the development of norms of time and workload of medical personnel. Moscow: Federal Research Institute for Health Organization and Informatics of Ministry of Health of the Russian Fed eration, 2013. 25 p. ( In Russ.).
5. Bossuyt PM, Reitsma JB, Bruns DE, et al. For the STARD Group. STARD 2015: An Updated List of Essential Items for Reporting Diagnostic Accuracy Studies. Radiology. 2015; 277(3): 826-832. doi : 10.1148/radiol.2015151516.
6. Morozov SP, Vladzimirskiy AV, Gombolevskiy VA, et al. Artificial intelligence in lung cancer screening: assessment of the diagnostic accuracy of the algorithm analyzing low-dose computed tomography. Tuberculosis and Lung Diseases. 2020; 98(8): 24-31. ( In Russ.). doi : 10.21292/2075-1230-2020-98-8-24-31.
7. Kaka H, Zhang E, Khan N. Artificial Intelligence and Deep Learning in Neuroradiology: Exploring the New Frontier. Can Assoc Radiol J. 2020 ; 18: 846537120954293. doi : 10.1177/0846537120954293.
8. Schoonenboom J., Johnson R.B. How to Construct a Mixed Methods Research Design. Kolner Z Soz Sozpsychol. 2017; 69(2): 107-131. doi : 10.1007/s11577-017-0454-1.
9. Wu G, Yang P, Xie Y, et al. Development of a clinical decision support system for severity risk prediction and triage of COVID-19 patients at hospital admission: an international multicentre study. Eur Respir J. 2020; 56(2): 2001104. doi : 10.1183/13993003.01104-2020.
10. Jalal S, Parker W, Ferguson D, Nicolaou S. Exploring the Role of Artificial Intelligence in an Emergency and Trauma Radiology Department. Can Assoc Radiol J. 2021; 72(1): 167-174. doi : 10.1177/0846537120918338.
11. Sohn JH, Chillakuru YR, Lee S, et al. An Open-Source, Vender Agnostic Hardware and Software Pipeline for Integration of Artificial Intelligence in Radiology Workflow. J Digit Imaging. 2020; 33(4): 1041-1046. doi : 10.1007/s10278-020-00348-8.
12. Blezek DJ, Olson-Williams L, Missert A, Korfiatis P. AI Integration in the Clinical Workflow. J Digit Imaging. 2021; 34(6): 1435-1446. doi : 10.1007/s10278-021-00525-3.
13. Kapoor N, Lacson R, Khorasani R. Workflow Applications of Artificial Intelligence in Radiology and an Overview of Available Tools. J Am Coll Radiol. 2020; 17(11): 1363-1370. doi : 10.1016/j.jacr.2020.08.016.
14. Davendralingam N, Sebire NJ, Arthurs OJ, Shelmerdine SC. Artificial intelligence in paediatric radiology: Future opportunities. Br J Radiol. 2021; 94(1117): 20200975. doi : 10.1259/bjr.20200975.
15. Ginat DT. Analysis of head CT scans flagged by deep learning software for acute intracranial hemorrhage. Neuroradiology. 2020; 62(3): 335-340. doi : 10.1007/s00234-019-02330-w.
16. Codari M, Melazzini L, Morozov SP, et al. Impact of artificial intelligence on radiology: a EuroAIM survey among members of the European Society of Radiology. Insights Imaging. 2019; 10(1): 105. doi : 10.1186/s13244-019-0798-3.
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For citation
Morozov S.P., Vladzymyrskyy A.V., Shulkin I.M., Ledikhova N.V., Arzamasov K.M., Andreychenko A.E., Logunova T.A., Omelyanskaya O.V., Gusev A.V. Feasibility of using artificial intelligence in radiation diagnostics. Medical doctor and information technology. 2022; 1: 12-29. (In Russ.). doi : 1025881/18110193_2022_1_12
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