Штучний інтелект

Науковий журнал

ISSN 2710-1673

ONLINE: ISSN 2710-1681

Шукати за:

Роком видання
Автором
Назвою статті

Використання штучного інтелекту в охороні здоров’я. Проблеми ідентифікації станів пацієнтів в процесах деталізації діагнозу

Мінцер О.П.1
1 Національний університет охорони здоров'я України імені Шупика
olgasukhan@gmail.com
https://orcid.org/0000-0002-7224-4886

Повний текст (PDF)

УДК: 534.843:004.9
Мова публікації: Англійська
Stuc. intelekt. 2023; 28; (1):8-11

Анотація: The problems of using artificial intelligence in health care were discussed. The aim of the study. Assess the possibilities of using artificial intelligence in medicine right now. Most studies comparing the performance of AI and clinicians are not valid because the tests are not large enough or come from different sources. This difficulty could be overcome in the era of an open healthcare system. Indeed, open data and open methods are sure to attract a lot of attention as new research methods. It also highlights the idea that AI technologies can improve accuracy by incorporating additional data for self-updating, but automatically incorporating low-quality data can lead to inconsistent or inferior algorithm performance. The conclusion made is that the introduction of artificial intelligence into clinical practice is a promising field of development that is rapidly developing along with other modern fields of precision medicine. One of the fundamental issues remains the solution of ethical and financial issues related to the introduction of artificial intelligence.

Ключові слова: Artificial intelligence, state identification, high-quality survey data, deep learning

Посилання:

  1. Newmarker C. Digital Surgery tout's artificial intelligence for the operating room. Medical Design and Outsourcing. Medical Design and Outsourcing; 2018. DOI:https://www.medicaldesignandoutsourcing.com/digital-surgery-touts-artificial-intelligence-for-the-operating-room/.
  2. Wijnberge M., Geerts B. F., Hol L., Lemmers N., Mulder M. P., Berge P., et al. Effect of a Machine Learning-Derived Early Warning System for Intraoperative Hypotension vs Standard Care on Depth and Duration of Intraoperative Hypotension During Elective Noncardiac Surgery: The HYPE Randomized Clinical Trial. JAMA 2020;323:l052–60.
  3. Khalsa R. K, Khashkhusha A., Zaidi S., Harky A., Bashir M. Artificial intelligence and cardiac surgery during the COVID-19 era. J Card Surg. 2021 May;36(5):1729-1733.
  4. Dias R. D., Shah J. A, Zenati M. A. Artificial intelligence in cardiothoracic surgery. Minerva Cardioangiol. 2020 Oct;68(5):532-538. DOI: 10.23736/S0026-4725.20.05235-4.
  5. Kilic A. Artificial Intelligence and Machine Learning in Cardiovascular Health Care. Ann Thorac Surg. 2020 May;109(5):1323-1329.
  6. Problems of developing AI systems based on neural networks. GRSE.2021. Available from:https://en.grse.de/blog/problems-of-developing-ai-systems-based-on-neural-networks/
  7. Loftus T. J, Upchurch G. R Jr., Bihorac A. Use of Artificial Intelligence to Represent Emergent Systems and Augment Surgical Decision-making. JAMA Surg. 2019;154(9):791-792.
  8. Dosis A., Aggarwal R., Bello F., Moorthy K., Munz Y., Gillies D. et al. Synchronized video and motion analysis for the assessment of procedures in the operating theater. Arch Surg 2005; 140:293–9.
  9. Dias R. D., Ngo-Howard M. C, Boskovski M. T., Zenati M. A., Yule S. J. Systematic review of measurement tools to assess surgeons' intraoperative cognitive workload. Br J Surg 2018; 105:491–501.
  10. Loftus T. J., et al. Artificial Intelligence and Surgical Decision-Making. JAMA Surg 2019.
  11. Dias R. D., Gupta A., Yule S. J. Using Machine Learning to Assess Physician Competence: A Systematic Review. Acad Med 2019; 94:427–39.
  12. Antuna V., Rennab F., Poonc C., Adcockd B., Hansena A. C. On instabilities of deep learning in image reconstruction and the potential costs of AI. – PNAS.-May 11, 2020.117 (48) 30088-30095. DOI: https://doi.org/10.1073/pnas.1907377117.
  13. Cui M., Zhang D. Y. Artificial intelligence and computational pathology. Lab Invest 101, 412–422 (2021). https://doi.org/10.1038/s41374-020-00514-0.
  14. Försch S., Klauschen F., Hufnagl P., Roth W. Artificial Intelligence in Pathology. Dtsch Arztebl Int. 2021 Mar 26;118(12):194-204. DOI: 10.3238/arztebl.m2021.0011. PMID: 34024323; PMCID: PMC8278129.
  15. Chang H. Y., Jung C. K., Woo J. I., Lee S, Cho J., Kim S. W., Kwak T. Y. Artificial Intelligence in Pathology. J Pathol Transl Med. 2019 Jan;53(1):1-12. DOI: 10.4132/jptm.2018.12.16. Epub 2018 Dec 28.
  16. Loftus T. J., Upchurch Gr. Jr, Bihorac A. Use of Artificial Intelligence to Represent Emergent Systems and Augment Surgical Decision-making. JAMA Surg. 2019;154(9):791-792.

Переглянути повний текст статті (PDF)