Artificial intellect and the main ethical problem of modern medicine


DOI: https://dx.doi.org/10.18565/therapy.2020.3.149-154

Yasnitsky L.N., Martynov A.I.

1 Perm State National Research University; 2 A.I. Evdokimov Moscow State University of Medicine and Dentistry of the Ministry of Healthcare of Russia
Despite the success of modern medicine, the important ethical problem of communication between doctor and patient still remains open. The matter of it is the fact that doctors often prescribe treatment courses for patients and observe: «will it help» or «will not help». If «does not help», they prescribe other drugs, etc. Physicists and engineers call this method of work «experimenting on a natural object» and in case of medicine this «object» is a living person. But experimenting with patients is immoral. Assumption that the development and application of artificial intelligence methods in medical practice will solve this important ethical problem is substantiated in the article. Before prescribing medications, doctors will have the opportunity for preliminarily testing and optimizing the treatment courses by performing virtual experiments not on patients themselves, but on their computer models.
Keywords: ethical problems in medicine, optimization of treatment courses, modeling of diseases development, neural circuitry, artificial intelligence, medicine
Read entire article

Literature


  1. Annas G.J., Grodin M.A. The Nazi Doctors and the Nuremberg Code. New York, NY: Oxford University Press Inc. 1992. Print.

  2. Чучалин А.Г., Черешнев В.А., Мишланов В.Ю. с соавт. Биоэтика, искусственный интеллект и медицинская диагностика. Пермь: Изд-во ПГМУ им. академика Е.А. Вагнера. 2019; 208 с. [Chuchalin A.G., Chereshnev V.A., Mishlanov V.Yu. et al. Bioethics, artificial Intelligence and medical diagnostics. Perm: Izdatel'stvo PGMU im. akademika E.A. Vagnera. 2019; 208 p. (In Russ.)].

  3. McCulloch W.S., Pitts W. A logical calculus of the ideas immanent in nervous activity. 1943. Bull Math Biol. 1990; 52(1–2): 99–115; discussion 73–97.

  4. Rosenblatt F. The perseptron: a probabilistic model for information storage and organization in the brain. Psychol Rev. 1958; 65(6): 386–408.

  5. Галушкин А.И. Об алгоритмах адаптации в многослойных системах распознавания образов. Доклады АН УССР (представлено акад. Глушковым В.М.). 1973; 91(1): 15–21. [Galushkin A.I. About adaptation algorithms in multilayer systems of pattern recognition. Reports of the Academy of sciences of UkrSSR. 1973; 91(1): 15–21 (In Russ.)].

  6. Werbos P.J. Beyond regression: new tools for prediction and analysis in the behavioral sciences. Phd Thesis, Dept. of Applied Mathematics. Harvard University, Cambridge. MA. 1974.

  7. Fukushima K. Neural network model for a mechanism of pattern recognition unaffected by shift in position – neocognitron. Transactions of the IECE. 1979; J62–A(10): 658–65.

  8. Ясницкий Л.Н., Думлер А.А., Черепанов Ф.М. Новые возможности применения методов искусственного интеллекта для моделирования появления и развития заболеваний и оптимизации их профилактики и лечения. Терапия. 2018; 1: 109–18. [Yasnitsky L.N., Dumler A.A., Cherepanov F.M. New possibilities of artificial intelligence method applications for to simulate the occurrence and development of diseases and optimize their prevention and treatment. Terapiya. 2018; 1: 109–18 (In Russ.)].

  9. Yasnitsky L.N., Dumler A.A., Cherepanov F.M. The Capabilities of artificial intelligence to simulate the emergence and development of diseases, optimize prevention and treatment thereof, and identify new medical knowledge. Journal of Pharmaceutical Science and Research. 2018; 10(9): 2192–200.

  10. Yasnitsky L.N., Dumler A.A., Bogdanov K.V. et al. Diagnosis and prognosis of cardiovascular diseases on the basis of neural networks. Biomedical Engineering. 2013; 47(3): 160–63. doi: 10.1007/s10527-013-9359-0.

  11. Yasnitsky L.N., Dumler A.A., Cherepanov F.M. Dynamic artificial neural networks as basis for medicine revolution. Advances in Intelligent Systems and Computing. 2019; 850: 351–58. Springer, Cham. doi: https://doi.org/10.1007/978-3-030-02351-5_40.

  12. Yasnitsky L.N. Whether be new «winter» of artificial intelligence? Lecture Notes in Networks and Systems. 2020; 78: 13–17. doi: https://doi.org/10.1007/978-3-030-22493-6_2.


About the Autors

Leonid N. Yasnitsky, candidate of engineering sciences, professor, professor of the Department of applied mathematics and computer science of Perm State National Research University. Address: 614990, Perm, 15 Bukireva Str. Tel.: +7 (342) 2-39-64-09. E-mail: yasn@psu.ru
Anatoly I. Martynov, MD, Academician of the Russian Academy of Sciences professor, professor of the Department of hospital therapy No. 1 of A.I. Yevdokimov Moscow State Medical and Dental University of the Ministry of Healthcare of Russia, president of RSMSIM. Address: 127473, Moscow, 20/1 Delegatskaya. Tel.: +7 (495) 609-67-00. E-mail: msmsu@msmsu.ru


Similar Articles

Back to Content

Бионика Медиа