Achievements and prospects for the application of artificial intelligence technologies in medicine. Overview. Part 2

Cover Page


Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription or Fee Access

Abstract

On a global scale, a radical transformation of the healthcare sector is taking place right before our eyes. The last few years have become a turning point in terms of the number of new directions, the emergence of innovative diagnostic and treatment methods, and the introduction of digital platforms. Digital medicine uses information and communication technologies to address the numerous problems associated with ensuring the quality and accessibility of medical care already available today. The rapid development of neural networks and artificial intelligence (AI) provides doctors with ample opportunities to predict the course of diseases and calculate the risks to the health of patients. Manufacturers of medical devices offer consumer a wide range of software and products with AI embedded. Despite the tremendous advances in the application of AI in medicine, the medical community is highly concerned about some of the intractable problems associated with the too rapid and ubiquitous use of these digital platforms. A highly trained neural network is an extremely complex computer program consisting of a large number of internal hidden layers with customizable parameters. The more complex the neural network and the number of computational operations it performs, the more difficult it is to understand the processes in its inner layers. The functioning of AI systems in a black box format makes explaining the results of its work a very non-trivial task. Therefore, in the future, research will certainly be required assessing the reliability of these systems and interpreting their decision-making processes which, will affect the neural networks of the latest generations.

Full Text

Restricted Access

About the authors

Vitaly A. Berdutin

Volga District Medical Center

Email: vberdt@gmail.com
ORCID iD: 0000-0003-3211-0899
SPIN-code: 8316-7111

 
 
Russian Federation, Nizhny Novgorod

Olga P. Abaeva

Sechenov First Moscow State Medical University (Sechenov University)

Author for correspondence.
Email: abaevaop@inbox.ru
ORCID iD: 0000-0001-7403-7744
SPIN-code: 5602-2435

MD, Dr. Sci. (Med.)

Russian Federation, Moscow

Tatyana E. Romanova

Sechenov First Moscow State Medical University (Sechenov University)

Email: romanova_te@mail.ru
ORCID iD: 0000-0001-6328-079X
SPIN-code: 4943-6121

MD, Cand. Sci. (Med.)

Russian Federation, Moscow

Sergey V. Romanov

Volga District Medical Center

Email: pomcdpo@mail.ru
ORCID iD: 0000-0002-1815-5436
SPIN-code: 9014-6344

MD, Dr. Sci. (Med.)

Russian Federation, Nizhny Novgorod

References

  1. Kim H, Goo JM, Lee KH, et al. Preoperative CT-based Deep Learning Model for Predicting Disease-Free Survival in Patients with Lung Adenocarcinomas. Radiology. 2020;296(1):216–224. doi: 10.1148/radiol.2020192764
  2. Phillips M, Marsden H, Jaffe W, et al. Assessment of accuracy of an artificial intelligence algorithm to detect melanoma in images of skin lesions. JAMA Netw Open. 2019;2(10):e1913436. doi: 10.1001/jamanetworkopen.2019.13436
  3. Questions and Answers on FDA’s Adverse Event Reporting System (FAERS) [Internet]. FDA [cited 23 January 2023]. Available from: https://www.fda.gov/drugs/surveillance/questions-and-answers-fdas-adverse-event-reporting-system-faers
  4. Liu X, Faes L, Kale AU, et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digital Health. 2019;1(6):e271–e297. doi: 10.1016/S2589-7500(19)30123-2
  5. Sabottke CF, Spieler BM. The effect of image resolution on deep learning in radiography. Radiol Artif Intell. 2020;2(1):e190015. doi: 10.1148/ryai.2019190015
  6. Heaven D. Why deep-learning AIs are so easy to fool. Nature. 2019;574(7777):163–166. doi: 10.1038/d41586-019-03013-5
  7. Habli I, Lawton T, Porter Z. Artificial intelligence in health care: accountability and safety. Bull World Health Organ. 2020;98(4):251–256. doi: 10.2471/BLT.19.237487
  8. Adamson AS, Smith A. Machine learning and health care disparities in dermatology. JAMA Dermatol. 2018;154(11):1247–1248. doi: 10.1001/jamadermatol.2018.2348
  9. Finlayson SG, Bowers JD, Ito J, et al. Adversarial attacks on medical machine learning. Science. 2019;363(6433):1287–1289. doi: 10.1126/science.aaw4399
  10. Zou J, Schiebinger L. AI can be sexist and racist — it’s time to make it fair. Nature. 2018;559(7714):324–326. doi: 10.1038/d41586-018-05707-8
  11. Kelly CJ, Karthikesalingam A, Suleyman M, et al. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019;17(1):195. doi: 10.1186/s12916-019-1426-2
  12. Nagendran M, Chen Y, Lovejoy CA, et al. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. BMJ. 2020;368:m689. doi: 10.1136/bmj.m689
  13. Willitts-King B, Bryant J, Holloway K. The humanitarian «digital divide». HPG Working Paper. London; 2019. P. 15.
  14. Filippov YuN, Abaeva OP, Filippov AYu. Problems of compensation of moral harm related to rendering of medical assistance. Meditsinskoe pravo. 2014;1:21–24. (In Russ).
  15. Voss C, Schwartz J, Daniels J, et al. Effect of wearable digital intervention for improving socialization in children with autism spectrum disorder: a randomized clinical trial. JAMA Pediatr. 2019;173(5):446–454. doi: 10.1001/jamapediatrics.2019.0285
  16. He J, Baxter SL, Xu J, et al. The practical implementation of artificial intelligence technologies in medicine. Nat Med. 2019;25(1):30–36. doi: 10.1038/s41591-018-0307-0
  17. Reshetnikov AM. The social institution of medicine. Part I. Sociology of Medicine. 2018;17(1):4–11. (In Russ). doi: 10.18821/1728-2810-2018-17-1-4-11
  18. Reshetnikov AM. The social institution of medicine. Part II. Sociology of Medicine. 2018;17(1):68–79. (In Russ). doi: 10.18821/1728-2810-2018-17-2-68-79

Supplementary files

Supplementary Files
Action
1. JATS XML

Copyright (c) 2022 Eco-Vector



СМИ зарегистрировано Федеральной службой по надзору в сфере связи, информационных технологий и массовых коммуникаций (Роскомнадзор).
Регистрационный номер и дата принятия решения о регистрации СМИ: серия ПИ № ФС 77 - 86498 от 11.12.2023 г. 
СМИ зарегистрировано Федеральной службой по надзору в сфере связи, информационных технологий и массовых коммуникаций (Роскомнадзор).
Регистрационный номер и дата принятия решения о регистрации СМИ: серия
ЭЛ № ФС 77 - 80649 от 15.03.2021 г.



This website uses cookies

You consent to our cookies if you continue to use our website.

About Cookies