ZAMONAVIY TIBBIYOT AXBOROT TIZIMLARI UCHUN JARAYON MODELLARINI LOYIHALASH MASALASI

Batirbek Samandarov; Shuxrat Tajibaev

Авторы

Batirbek Samandarov Muhammad al-Xorazmiy nomidagi Toshkent Axborot Texnologiyalari Universiteti
Shuxrat Tajibaev Berdaq nomidagi Qoraqalpoq davlat universiteti

Ключевые слова:

sog‘liqni saqlashni raqamlashtirish, tibbiyot axborot tizimlari, jarayon modellari, tibbiyotda sun’iy intellektning o‘rni

Аннотация

Bugungi kunda sog‘liqni saqlashni raqamlashtirish va sun’iy intellekt texnologiyalarini joriy qilish tibbiy xizmat sifatini tubdan takomillashtirish va yaxshilash uchun ulkan salohiyatga ega bo‘lgan asosiy sohalardan biri sanaladi. So‘nggi yillarda raqamli texnologiyalarning jadal rivojlanishi va sun’iy intellektdan sog‘liqni saqlashning tashxislash va davolashdan tortib shifoxona boshqaruvi va sog‘liqni saqlash tizimlarigacha bo‘lgan turli sohalarida qo‘llanilishi sohasida ko‘plagan tadqiqotlar olib borilayotganiga guvox bo‘lyapmiz. Ushbu maqolada tibbiyotni raqamlashtirish, tashxislash va sun’iy intellekt texnologiyalarining sog‘liqni saqlash sohasidagi o‘rni va axborot tizimlariga zaruratlar, shuningdek, ularni loyihalashtirishda duch keladigan foydali jihatlar va keltirib chiqarishi mumkin bo‘lgan muammolar hamda ushbu sohadagi tadqiqotlar sharxi bayon qilingan.

Библиографические ссылки

B.H. Li, B.C. Hou, W.T. Yu, X.B. Lu, C.W. Yang. Applications of artificial intelligence in intelligent manufacturing: A review // Frontiers of Information Technology & Electronic Engineering, 18 (1) (2017), pp. 86-96

O. Ali, p. Murray, S. Muhammed, Y.K. Dwivedi, S Rashiti. Evaluating organizational level IT innovation adoption factors among global firms // Journal of Innovation & Knowledge, 7 (3) (2022), Article 100213

Kaplan, M. Haenlein. Rulers of the world, unite! The challenges and opportunities of artificial intelligence // Business Horizons, 63 (1) (2020), pp. 37-50

Nishanov A.X., Akbaraliev B.B., Samandarov B.S., Tajibaev Sh.X., Akhmedov O.K. An Algorithm for Classification, Localization and Selection of Informative Features in the Space of Politypic Data // Webology. Volume 17(1), Jun, 2020. pp.341-364. DOI: 10.14704/WEB/V17I1/WEB17009

Mo’minov B.B., Egamberdiyev, E. A Neuro-Fuzzy Model for Predicting Successful Passing of Entrance Exams of Applicants to Higher Education. Central Asian Journal of Education and Computer Sciences (CAJECS), 2023, vol. 2(2), pp.6–13.

C.F. Chien, S. Dauzère-Pérès, W.T. Huh, Y.J. Jang, J.R. Morrison. Artificial intelligence in manufacturing and logistics systems: algorithms, applications, and case studies // International Journal of Production Research, 58 (9) (2020), pp. 2730-2731

P. Kumar, S.K. Sharma, V. Dutot. Artificial intelligence (AI)-enabled CRM capability in healthcare: The impact on service innovation // International Journal of Information Management, 69 (2023), Article 102598, doi: 10.1016/j.ijinfomgt.2022.102598

Z. Chen. An AI-Based heart failure treatment adviser system // IEEE Journal of Translational Engineering in Health and Medicine, 6 (2018), pp. 1-10

N. Dhieb, H. Ghazzai, H. Besbes, Y Massoud. A Secure AI-Driven architecture for automated insurance systems: Fraud detection and risk measurement // IEEE Access, 8 (2020), pp. 58546-58558

H. Yu, Z. Zhou. Optimization of IoT-based artificial intelligence assisted telemedicine health analysis system // IEEE Access, 9 (2021), pp. 85034-85048

M.I. Merhi. An evaluation of the critical success factors impacting artificial intelligence implementation // International Journal of Information Management (2022), Article 102545, doi: 10.1016/j.ijinfomgt.2022.102545

Shaikha F. S. Alhashmi, Said A. Salloum, Sherief Abdallah. Critical Success Factors for ImplementingArtiﬁcial Intelligence (AI) Projects in DubaiGovernment United Arab Emirates(UAE) Health Sector: Applying the ExtendedTechnology Acceptance Model (TAM) // International Conference on Advanced Intelligent Systems and Informatics. AISI 2019: Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2019 pp 393–405

Using artificial intelligence for early detection and treatment of illnesses / [Elektron manba] https://www.sciencedaily.com/releases/2021/08/210820135346.htm (Murojaat sanasi 10-05-2023)

Cucchi M. et al. Reservoir computing with biocompatible organic electrochemical networks for brain-inspired biosignal classification //Science Advances. – 2021. – Т. 7. – №. 34. – С. eabh0693.

Fitzgerald, R.C., Antoniou, A.C., Fruk, L. et al. The future of early cancer detection. Nat Med 28, 666–677 (2022). https://doi.org/10.1038/s41591-022-01746-x

M.T. Sqalli, D. Al-Thani. AI-supported health coaching model for patients with chronic diseases // The 16th International Symposium on Wireless Communication Systems (2019), pp. 452-456

L. Zhou. A rapid, accurate and machine-agnostic segmentation and quantification method for CT-Based COVID-19 diagnosis // IEEE Transactions on Medical Imaging, 39 (8) (2020), pp. 2638-2652

Tobore I. et al. Deep learning intervention for health care challenges: some biomedical domain considerations //JMIR mHealth and uHealth. – 2019. – Т. 7. – №. 8. – С. e11966.

B. Wahl, A. Cossy-Gantner, S. Germann, NR. Schwalbe. Artificial intelligence (AI) and global health: how can AI contribute to health in resource-poor settings? // BMJ global health, 3 (4) (2018), Article e000798

A. Kaur, R. Garg, P. Gupta. Challenges facing AI and big data for resource-poor healthcare system // The 2nd International Conference on Electronics and Sustainable Communication Systems (2021), pp. 1426-1433

L.G. Pee, S. Pan, L. Cui. Artificial intelligence in healthcare robots: A social informatics study of knowledge embodiment // Journal of the Association for Information Science and Technology (2019), pp. 1-38

P.K. Maduri, Y. Dewangan, D. Yadav, S. Chauhan, K. Singh. IoT based patient health monitoring portable Kit // The 2ndInternational Conference on Advances in Computing, Communication Control and Networking (2020), pp. 513-516

C. Comito, D. Falcone, A. Forestiero. Current trends and practices. In smart health monitoring and clinical decision support // IEEE International Conference on Bioinformatics and Biomedicine (2020), pp. 2577-2584