doi: 10.18178/ijmlc.2019.9.6.885
Logical, Philosophical and Ethical Aspects of AI in Medicine
Oleg O. Varlamov1,2
,
Dmitry A. Chuvikov2 ,
Larisa E. Adamova2 ,
Maxim A. Petrov3 ,
Irina K. Zabolotskaya3 ,
Tatyana N. Zhilina3
- 1Bauman Moscow State Technical University (Bauman University – National Technological University), Moscow, 105005, Russia
- 2Mivar Ltd, Moscow, 127521, Russia
- 3Institute of Social Sciences of I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), Moscow, 119991, Russia
- Manuscript receivedSeptember 3, 2019
- revisedOctober 22, 2019
Abstract
The logical type of Artificial Intelligence is presented in the article. MIVAR (Multidimensional Informational Variable Adaptive Reality) technology is based on the gnoseological triplet concept “Thing – Property – Relationship.” The unlimited number of MIVAR units fill the MIVAR space that makes it communicatory, discrete, and scalable. MIVAR information processing allows creating complicated algorithms for medicine. We have chosen the clinical model of pain in the heart for preparation ontology for doctors. Knowledge expert model MIVAR WiMi “Chest Pain” is shown. The logical Artificial Intelligence (expert model) can help doctors fast, precisely and in the best way in the decision-making process. Except medical ontology Artificial Intelligence must have an empathic, emotional experience, without that medical care cannot be imagined. MIVAR technology is the closest to human thinking among other Artificial Intelligence technologies; it differs from the Artificial network of Mirror neurons, related to emotional perception, but MIVAR space with rules and constraints can imitate emotions and empathy.
Keywords
- Artificial intelligence
- clinical model
- empathy
- ethics
- MIVAR
- pain in heart
How to Cite
Oleg O. Varlamov, Dmitry A. Chuvikov, Larisa E. Adamova, Maxim A. Petrov, Irina K. Zabolotskaya, and Tatyana N. Zhilina, "Logical, Philosophical and Ethical Aspects of AI in Medicine," International Journal of Machine Learning, vol. 9, no. 6, pp. 868-873, 2026. https://doi.org/10.18178/ijmlc.2019.9.6.885
Copyright & License
Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).