Explainable artificial intelligence for medicine
https://doi.org/10.25881/18110193_2022_2_4
Abstract
The success and wide-range applications of artificial intelligence (AI) technologies and, in particular, deep learning neural networks methods have led us to a clear understanding of two main problems: the problem of errors (the reliability problem) and the problem of explicitly explaining the decisions made by AI (the explanability problem). These problems are closely related: unexplained AI errors can happen again and again. This is completely unacceptable from the perspective of AI applications in health care because it is critical to the lives and health of patients. If left unresolved, the problems of error and explainability can lead to the rejection or significant restriction of AI systems in medical applications. In this paper, we discuss the problems of explainable artificial intelligence (XAI) for medicine and consider different approaches to solving them.
About the Authors
O. E. Karpov
Pirogov National Medical and Surgical Center
Russian Federation
Russian Federation
Academician of the RAS, Dr. Sci. (Medicine), Professor
Moscow
D. A. Andrikov
Immersmed LLC
Russian Federation
Russian Federation
PhD
Moscow
V. A. Maksimenko
Innopolis University
Russian Federation
Russian Federation
Dr. Sci.
Kazan
A. E. Hramov
Immanuel Kant Baltic Federal University
Russian Federation
Russian Federation
Dr. Sci., Professor
Kaliningrad
References
1. CB Insights Research. Healthcare remains the hottest AI category for deals. April 12, 2017. [Last accessed on 2022 Feb 11]. Available at: https://www.cbinsights.com/research/artificial-intelligence-healthcare-startups-investors.
2. На основе данных State of AI Q3’21. [Last accessed on 2022 March 22]. Available at: https://www.cbinsights.com/research/report/ai-trends-q3-2021.
3. He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K. The practical implementation of artificial intelligence technologies in medicine. Nature medicine. 2019; 25(1): 30-36.
4. Johnson Kevin B, et al. Precision medicine, AI, and the future of personalized health care. Clinical and translational science. 2021;14(1): 86-93.
5. Karpov OE, Hramov AE. Prognosticheskaya medicina. Vrach i informacionnye tekhnologii. 2021; 3: 20-37. (In Russ). doi: 10.25881/18110193_2021_3_20.
6. Strabo K, Tavel JA. What are biomarkers? Current Opinion in HIV and AIDS. 2010; 5(6): 463.
7. Toprol E.J. Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books, New York, NY, 2019.
8. Kundu S. AI in medicine must be explainable. Nature Medicine. 2021; 27(8): 1328.
9. Ashby WR. An Introduction to Cybernetics. Chapman & Hall Ltd., London, UK, 1957.
10. Friedman CP. A «fundamental theorem» of biomedical informatics. J. Am. Med. Inform. Assoc. 2009; 16: 169-170.
11. Karpov OE, Grubov VV, Maksimenko VA, Utaschev N, Semerikov VE, Andrikov DA, Hramov AE. Noise amplification precedes extreme epileptic events on human EEG. Physical Review E. 2021; 103: 022310.
12. Perera P, Patel VM. Learning deep features for one-class classification. IEEE Transactions on Image Processing. 2019; 28(11): 5450-5463.
13. Kuchin AS, Grubov VV, Maksimenko VA, Utyashev NP. Avtomatizirovannoe rabochee mesto vracha epileptologa s vozmozhnost’yu avtomaticheskogo poiska pristupov epilepsii. Vrach i informacionnye tekhnologii. 2021; 3: 62-73. (In Russ). doi: 1025881/18110193_2021_3_62.
14. Chaudhary U, Birbaumer N, Ramos-Murguialday A. Brain–computer interfaces for communication and rehabilitation. Nature Reviews Neurology. 2016; 12(9): 513-525.
15. Shah RB, Patel M, Maahs DM, Shah V N. Insulin delivery methods: Past, present and future. International journal of pharmaceutical investigation. 2016; 6(1): 1.
16. Ricci RP, Morichelli L, Santini M. Home monitoring remote control of pacemaker and implantable cardioverter defibrillator patients in clinical practice: impact on medical management and health-care resource utilization. Europace. 2008; 10(2): 164-170.
17. Hramov AE, Maksimenko VA, Pisarchik AN. Physical principles of brain–computer interfaces and their applications for rehabilitation, robotics and control of human brain states. Physics Reports. 2021; 918: 1-133.
18. Sarkisova K, van Luijtelaar G. The WAG/Rij strain: a genetic animal model of absence epilepsy with comorbidity of depressiony. Progress in Neuro-Psychopharmacology and Biological Psychiatry. 2011; 35(4): 854-876.
19. van Luijtelaar G, Lüttjohann A, Makarov VV, Maksimenko VA, Koronovskii AA, Hramov AE. Methods of automated absence seizure detection, interference by stimulation, and possibilities for prediction in genetic absence models. Journal of Neuroscience Methods. 2016; 260: 144-158.
20. Мaksimenko VA, Heukelum S, Makarov VV, Kelderhuis J, Lüttjohann A, Koronovskii AA, Hramov AE, Luijtelaar G. Absence Seizure Control by a Brain Computer Interface. Scientific Reports. 2017; 7: 2487.
21. Budde B, Maksimenko V, Sarink K, Seidenbecher T, van Luijtelaar G, Hahn T, Pape HC, Lüttjohann Seizure prediction in genetic rat models of absence epilepsy: improved performance through multiple-site cortico-thalamic recordings combined with machine learning. Eneuro. 2022; 9(1).
Review
For citations:
Karpov O.E., Andrikov D.A., Maksimenko V.A., Hramov A.E. Explainable artificial intelligence for medicine. Medical Doctor and Information Technologies. 2022;(2):4-11. (In Russ.) https://doi.org/10.25881/18110193_2022_2_4
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