Images in Logical-and-Linguistic Artificial Intelligence Systems

  • Boris A. Kobrinskii Federal Research Center “Computer Science and Control” of Russian Academy of Science
Keywords: logic-and-linguistic-and-image system, holistic images, confidence factors, visual image number, fuzzy logic, orphan hereditary diseases


Visual images are holistic, and when verbalized there is a partial loss of semantic content. However, it should be noted the lack of effectiveness of decision support systems only when using images without an effective context, and systems that do not include holistic images. Inclusion of images in the knowledge base of intelligent systems can significantly improve their effectiveness. At the stage of formation of intermediate diagnostic hypotheses, the system will present to the user (physician) a hypothesis specific to the verbal and visual characteristics. At the same time, it is necessary to take into account the need to use fuzzy logic at the stages of the derivation of solutions. The subsequent process will depend on the physician’s confidence in the coincidence of the image of the diagnosed patient with the image(s) in the knowledge base of the intellectual system.

Author Biography

Boris A. Kobrinskii, Federal Research Center “Computer Science and Control” of Russian Academy of Science
PhD, Dr. med. sci., Prof. Head of the Department of "Clinical decision support system"


(1) Paivio A. Imagery and verbal processes. New York, Holt, Rinehart & Winston, 1971.

(2) Kobrinskii B.A. Approaches to the construction of cognitive linguistic-image models of knowledge representation for medical intelligent systems. Sci Tech Inform Process 2016; 43: 289-295.

(3) Kobrinskii B.A. Sequences of Images in Intelligent Systems. Sci Tech Inform Process 2010; 37: 328-335.

(4) Stadler B.M.R., Stadler P.F., Wagner G.P., Fontana W. The Topology of the Possible: Formal Spaces Underlying Patterns of Evolutionary Change. J Theor Biol 2001; 213: 241-274. DOI: 10.1006/jtbi.2001.2423

(5) Richardson J.T.E. Imagery. Abingdon: Taylor & Francis Group, Psychology Press, 1999.

(6) Solso R.L., MacLin O.H., MacLin M. Cognitive Psychology (8th ed.). Yorkshire, Pearson, 2008.

(7) Pawlak Z. Rough set theory and its applications. J Telecom Inform Technol 2002; 3: 7-10.

(8) Zhang Q., Xie Q., Wang G. A survey on rough set theory and its applications. CAAI Transact Intell Technol 2016; 1: 323-333 (open access). DOI: 10.1016/J.TRIT.2016.11.001.

(9) Voinov A.V. The role of similarity judgment in intuitive problem solving and its modeling in a sheaf-theoretic framework. In: L. Wang, S. Halgamuge and X. Yao, eds. FSKD’02: Proceedings of the 1st International Conference on Fuzzy Systems and Knowledge Discovery.

Vol.2. Singapore: Orchid Country Club, November 18-22, 2002. P.753-

(10) Hüllermeier E. Similarity-based inference as evidential reasoning. Int J Approx Reason 2001; 26: 67-100. Doi: 10.1016/S0888-613x(00)00062-1

(11) Esteva F., Garcia P., Godo L. and Rodrýguez R. A modal account of similarity-based reasoning. Int J Approx Reason 1997; 16: 235-260.

(12) Yang L., Jin R., Mummert L., et al. A Boosting Framework for Visuality-Preserving Distance Metric Learning and Its Application to Medical Image Retrieval. IEEE Trans Pattern Anal Mach Intell 2010; 32:

–44. DOI: 10.1109/TPAMI.2008.273.

(13) Baraitser M. and Winter R.M. London Dysmorphology Database, London Neurogenetics Database & Dysmorphology Photo Library on CD-ROM. 3rd ed. Oxford: Oxford University Press, 2001.

(14) Aymé S. Orphanet, an information site on rare diseases. Soins 2003; 672:46-47.

(15) Hammond P., Hutton T.J., Allanson J.E., et al., Discriminating Power

of Localized Three-Dimensional Facial Morphology. Am J Hum Genet 2005; 77:999-1010. DOI: 10.1086/498396

(16) Vardell E., Bou-Crick C., VisualDx: A Visual Diagnostic Decision Support Tool. Med Ref Serv Q 2012; 31:414-424. DOI: 10.1080/02763869.2012.724287

(17) Kuru K., Niranjan M., Tunca Y., et al. Biomedical visual data analysis to build an intelligent diagnostic decision support system in medical genetics. Artif Intell Med 2014; 62: 105–118.

(18) Seitinger A., Fehre K., Adlassnig K.P., et al. An Arden-Syntax-based clinical decision support framework for medical guidelines – Lyme borreliosis as an example. Stud Health Technol Inform 2014; 198: 125-

How to Cite
Kobrinskii, B. A. (2019). Images in Logical-and-Linguistic Artificial Intelligence Systems. Journal of Biomedical Engineering and Medical Imaging, 6(1), 01.