CONCEPTS OF CREATING AN INTELLIGENT MEDICAL DIAGNOSTIC SYSTEM TO ASSIST IN THE WORK AND TRAINING OF DOCTORS BASED ON ARTIFICIAL INTELLIGENCE

Authors

DOI:

https://doi.org/10.32689/maup.it.2023.5.4

Keywords:

diagnostics, intelligent information system, training, forecasting, artificial intelligence.

Abstract

Abstract. The article is devoted to the development of intelligent medical diagnostic systems using information systems and technologies. The article provides an overview of the current state and development of information systems, technologies and artificial intelligence in the medical field, analyzes existing intelligent diagnostic systems in medicine and cardiology in particular, and proves the need to create intelligent systems to help primary care physicians and cardiologists. The purpose of the publication was to investigate the current state of intelligent medical diagnostic systems, analyze the shortcomings of such systems, determine the feasibility of creating a new diagnostic system, and formulate principles and criteria for its operation. The scientific novelty of the article is a new approach for diagnosing and treating patients, as well as training medical professionals using an intelligent medical system based on artificial intelligence. Leveraging advanced AI algorithms, the system analyzes vast datasets encompassing patient records, medical literature, and real-time clinical data to provide accurate and timely diagnostic insights. The intelligent medical diagnostic system operates as a supportive tool, aiding doctors in the diagnostic process by offering refined suggestions, identifying potential anomalies, and recommending personalized treatment plans. By harnessing machine learning and deep learning techniques, the system continuously adapts and evolves, learning from each diagnostic scenario and refining its predictive accuracy over time. Through interactive simulations and case-based learning modules, aspiring and practicing doctors can engage in immersive, realistic scenarios, honing their diagnostic skills and expanding their knowledge base. The disadvantages of this article include only a theoretical approach to the formation of concepts and tasks for an intelligent medical system, and a review of existing systems.

References

Національне дослідження STEPS в Україні. URL: https://phc.org.ua/naukova-diyalnist/doslidzhennya/doslidzhennya-z-neinfekciynikh-zakhvoryuvan/nacionalne-doslidzhennya-steps-v-ukraini

Центр громадського здоров’я МОЗ України. Серцево-судинні захворювання – головна причина смерті українців. 2021. URL: https://phc.org.ua/naukova-diyalnist/doslidzhennya/doslidzhennya-z-neinfekciynikh-zakhvoryuvan/nacionalne-doslidzhennya-steps-v-ukraini

Коробка О. Практичні рекомендації щодо ведення пацієнтів з артеріальною гіпертензією. Кардіологія, Ревматологія, Кардіохірургія. 2020. № 4 (71) С. 25-27 URL: https://health-ua.com/multimedia/userfiles/files/2020/Cardio_4_2020/Cardio_4_2020_st25_27.pdf

Лисенко Г.І., Ященко О.Б. Медикаментозне лікування пацієнтів із артеріальною гіпертензією. 2011. Укр. Мед. Часопис, 3 (83) – V/VI. URL: https://api.umj.com.ua/wp/wp-content/uploads/2011/06/3002.pdf

Інтелектуальна інформаційна система. URL: https://pidru4niki.com/74257/informatika/intelektualna_informatsiyna_sistema

Висоцький А.А., Суріков О.О., Василюк-Зайцева С.В. Розвиток штучного інтелекту в сучасній медицині. 2023.Укр. Мед. Часопис 2 (154) – III/IV

Москаленко А. С. Інтелектуальна система підтримки прийняття рішень для радіонуклідної діагностики в кардіології. 2016. Радіоелектронні і комп’ютерні системи № 3. С. 49–55 URL: http://www.irbis-nbuv.gov.ua/cgi-bin/

irbis_nbuv/cgiirbis_64.exe?I21DBN=LINK&P21DBN=UJRN&Z21ID=&S21REF=10&S21CNR=20&S21STN=1&S21FMT=A

SP_meta&C21COM=S&2_S21P03=FILA=&2_S21STR=recs_2016_3_8

Betancur J., Commandeur F., Motlagh M. Deep Learning for Prediction of Obstructive Disease From Fast Myocardial Perfusion SPECT: A Multicenter Study. 2018. URL: https://www.jacc.org/doi/abs/10.1016/j.jcmg.2018.01.020

Kwon J.M., Lee S.Y., Jeon K.H., Lee Y. Deep Learning–Based Algorithm for Detecting Aortic Stenosis Using Electrocardiography. 2020. URL: https://www.ahajournals.org/doi/full/10.1161/JAHA.119.014717

Khurshid S., Friedman S., Reeder C. ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation. 2021. URL: https://www.ahajournals.org/doi/full/10.1161/CIRCULATIONAHA.121.057480

Zachi I. Attia, Kapa S., Lopez-Jimenez F. Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram. 2019. URL: https://www.nature.com/articles/s41591-018-0240-2

Khurshid S., Friedman S., Pirruccello J.P. Deep Learning to Predict Cardiac Magnetic Resonance–Derived Left Ventricular Mass and Hypertrophy From 12-Lead ECGs. 2021. URL: https://www.ahajournals.org/doi/full/10.1161/CIRCIMAGING.120.012281

Arnaout R., Curran L., Zhao Y. An ensemble of neural networks provides expert-level prenatal detection of complex congenital heart disease. 2021. URL: https://www.nature.com/articles/s41591-021-01342-5%C2%A0

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Published

2024-01-09

How to Cite

KOLOMOIETS, S. (2024). CONCEPTS OF CREATING AN INTELLIGENT MEDICAL DIAGNOSTIC SYSTEM TO ASSIST IN THE WORK AND TRAINING OF DOCTORS BASED ON ARTIFICIAL INTELLIGENCE. Information Technology and Society, (5 (11), 28-33. https://doi.org/10.32689/maup.it.2023.5.4