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Formulating tasks, interpretation, and planning the implementation of research results using artificial intelligence in medicine
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UDC: 61:004.8:001.891
Publication Language: English
Stuc. intelekt. 2024; 29(1):10-17
Abstract: Strategic issues of artificial intelligence use in medicine are considered. Summarizing, as of today, AI supports doctors but does not replace them. It is emphasized that AI in healthcare typically solves important, but rather limited in scope, tasks. Difficulties in further implementation of AI are analyzed. The aim of the study was to address the analytical generalization of AI capabilities in healthcare, analyze the problems of using the Universum of medical-biological knowledge as a global unified resource, and conceptually justify the need to structure medical-biological knowledge, introducing fundamentally new forms of knowledge transfer in healthcare.
Keywords: artificial intelligence, machine learning, deep learning, knowledge Universum, structured and unstructured data, coreference of medical information, knowledge mobilization, coding of medical information.
References:
- Beam, A.L., Drazen, J.M., Kohane, I.S., Leong, T.Y., Manrai, A.K., Rubin, E.J. (2023) Artificial Intelli-gence in Medicine. N Engl J Med. 388(13):1220-1221. https://doi.org/10.1056/NEJMe2206291.
- Haug, C.J., Drazen, J.M. (2023) Artificial Intelligence and Machine Learning in Clinical Medicine, 2023. N Engl J Med, 388(13):1201-1208. https://doi.org/10.1056/NEJMra2302038.
- Buch, V.H., Ahmed, I., Maruthappu, M. Artifi-cial intelligence in medicine: current trends and future possibilities. Br J Gen Pract. 2018 Mar;68(668):143-144. https://doi.org/10.3399/bjgp18X695213.
- Khan, B., Fatima, H., Qureshi, A., Kumar, S., Hanan, A., Hussain, J., Abdullah, S. Drawbacks of Artificial Intelligence and Their Potential Solutions in the Healthcare Sector. (2023) Biomed Mater Devices. 8:1-8. https://doi.org/10.1007/s44174-023-00063-2
- Lazer, D., Kennedy, R., King, G., Vespignani, A. (2014). Big data. The parable of Google Flu: traps in big data analysis. Science. 343(6176):1203-5. https://doi.org/10.1126/science.1248506.
- Mintser, O., Sinyenko, N. O. (2023). Data discrimination in pathomorphology. ways of coping. Medical Informatics and Engineering, (3), 7–10. https://doi.org/10.11603/mie.1996-1960.2022.3.13359
- Alamsyah, Riza., Luhur, Budi., Wiranata, A. D., Luhur, B. (2019) Defect Detection of Ceramic Tiles using Median Filtering, Morphological Techniques, Gray Level Cooccurrence Matrix and K-Nearest Neighbor Method. Scientific Research Journal, 4(6):2201-2796 https://doi.org/10.31364/SCIRJ/v7.i4.2019.P0419632
- Ammar, M., Rania, K. (2023) A comprehensive review on ensemble deep learning: Opportunities and challenges. Journal of King Saud University - Com-puter and Information Sciences. 35(2):757-774 https://doi.org/10.1016/j.jksuci.2023.01.014
- Kothari, S., Phan, J.H., Stokes, T.H., Wang, M.D. (2013) Pathology imaging informatics for quanti-tative analysis of whole-slide images. J. Am. Med. Inform. Assoc., 20 ,1099-1108 https://doi.org/10.1136/amiajnl-2012-001540
- Khosravi, P., Kazemi, E., Imielinski, M., Elemento O., Hajirasouliha, I. (2018) Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images. EBioMedicine. 27:317-328. https://doi.org/10.1016/j.ebiom.2017.12.026
- Greenspan H., van Ginneken B., Summers R.M. (2016) Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique IEEE Trans. Med. Imaging, 35:1153-1159 https://doi.org/10.1109/TMI.2016.2553401
- Chaffey, D. (2023) Search engine marketing statistics 2023. The Search Engine Optimisation (SEO) toolkit contains 16 Feb, 2023 https://www.smartinsights.com/search-engine-marketing/search-engine-statistics/
- Golhasany, H., Harvey, B. (2023) Capacity development for knowledge mobilization: a scoping review of the concepts and practices. Humanit Soc Sci Commun 10: 235 https://doi.org/10.1057/s41599-023-01733-8
- Khakpour, A. (2020) Effectiveness of Knowledge Acquisition in Medical Education: An argumentative literature review of the resources’s requirements. Future of Medical Education Journal. 10(3): 56-63. https://doi.org/10.22038/FMEJ.2020.40998.1271
- Ley T. (2020) Knowledge structures for integrat-ing working and learning: A reflection on a decade of learning technology research for workplace learning. Br J Educ Technol. 51(2): 331-346. https://doi.org/10.1111/bjet.12835
- Jesacher-Roessler, Livia. (2021) The travel of ideas: the dual structure of mobilized knowledge in the context of professional learning networks. Journal of Professional Capital and Community. ahead-of-print. https://doi.org/10.1108/JPCC-06-2020-0048