The use of artificial intelligence in medical morphology and medical education

Authors

DOI:

https://doi.org/10.26641/1997-9665.2025.3.191-195

Keywords:

artificial intelligence, histology, medical education, digital technologies, VR/AR technologies, convolutional neural networks.

Abstract

Background. Modern science is experiencing a stage of rapid development due to the integration of innovative digital technologies, among which artificial intelligence (AI) occupies a leading position. In morphology, AI opens up new opportunities for analyzing large datasets, automating image analysis, and modeling complex processes. In the field of medical education, the implementation of AI transforms traditional approaches to teaching morphological disciplines and determines new directions for the development of medical training. Objective. To analyze the possibilities and approaches to the use of AI in medical morphology and in the educational process of histology, cytology, and embryology. Methods. The study employed comprehensive methods of practical orientation, including analysis, synthesis, induction, and deduction, as well as specialized methods such as component analysis. Results. The use of AI in morphological science enables the automation of cell and tissue analysis, the identification of subtle patterns, and the creation of large-scale digital databases of histological images. The application of CNNs, U-Net, and Vision Transformers allows automated histological slide analysis, improves morphometric accuracy, and ensures standardized evaluation of morphological changes. In the educational process, the integration of digital platforms, virtual microscopes and simulators (Labster, Anatomage, Organon, QuPath, PathPresenter), VR/AR technologies, and Explainable AI provides interactivity, personalization, and deep immersion of students into the structure of tissues and organs, fostering the development of critical thinking and analytical skills. Conclusion. The use of AI in morphology and histology teaching is not only a technological trend but also a strategic direction in the development of medicine and education. The integration of digital platforms, virtual laboratories, and VR/AR systems makes learning interactive, personalized, and practice-oriented. The combination of traditional teaching methods with AI enhances motivation, cultivates critical thinking, and prepares future physicians for work in the era of digital medicine.

References

  1. Grzybowski A, Pawlikowska-Łagód K, Lambert WC. A History of Artificial Intelligence. Clin Dermatol. 2024;42(3):221-9. doi: 10.1016/j.clindermatol.2023.12.016.
  2. El Arab RA, Alkhunaizi M, Alhashem YN, Al Khatib A, Bubsheet M, Hassanein S. Artificial intelligence in vaccine research and development: an umbrella review. Front Immunol. 2025;16:1567116. doi: 10.3389/fimmu.2025.1567116.
  3. Dwivedi YK, Sharma A, Rana NP, Giannakis M, Goel P, Dutot V. Evolution of artificial intelligence research in Technological Forecasting and Social Change: Research topics, trends, and future directions. Technol Forecast Soc Change. 2023;192. doi: 10.1016/j.techfore.2023.122579.
  4. Ali MU, Kim KS, Khalid M, Farrash M, Zafar A, Lee SW. Enhancing Alzheimer's disease diagnosis and staging: a multistage CNN framework using MRI. Front Psychiatry. 2024;15:1395563. doi: 10.3389/fpsyt.2024.1395563.
  5. El-Assy AM, Amer HM, Ibrahim HM, et al. A novel CNN architecture for accurate early detection and classification of Alzheimer’s disease using MRI data. Sci Rep. 2024;14:3463. doi: 10.1038/s41598-024-53733-6
  6. Giarnieri E, Scardapane S. Towards Artificial Intelligence Applications in Next Generation Cytopathology. Biomedicines. 2023;11(8):2225. doi: 10.3390/biomedicines11082225.
  7. Liu G, Dong F, Fu C, Smith ZJ. Automated morphometry toolbox for analysis of microscopic model organisms using simple bright-field imaging. Biol Open. 2019;8(3):bio037788. doi: 10.1242/ bio.037788. PMID: 30814065.
  8. Ruffinatti FA, Genova T, Mussano F, Munaron L. MORPHEUS: An automated tool for unbiased and reproducible cell morphometry. J Cell Physiol. 2020;235(12):10110-5. doi: 10.1002/jcp.29768.
  9. Bourn MD, Daly LF, Huggett JF, Braybrook J, Rivera JF. Evaluation of image analysis tools for the measurement of cellular morphology. Front Cell Dev Biol. 2025;13:1572212. doi: 10.3389/ fcell.2025.1572212.
  10. Shafi S, Parwani AV. Artificial intelligence in diagnostic pathology. Diagn Pathol. 2023;18(1):109. doi: 10.1186/s13000-023-01375-z.
  11. Baghdadi A, Lama S, Singh R, et al. Tool-tissue force segmentation and pattern recognition for evaluating neurosurgical performance. Sci Rep. 2023;13:9591. doi: 10.1038/s41598-023-36702-3.
  12. Canales-Fiscal MR, Tamez-Peña JG. Hybrid morphological-convolutional neural networks for computer-aided diagnosis. Front Artif Intell. 2023;6:1253183. doi: 10.3389/frai.2023.1253183.
  13. Gedefaw L, Liu CF, Ip RKL, Tse HF, Yeung MHY, Yip SP, Huang CL. Artificial Intelligence-Assisted Diagnostic Cytology and Genomic Testing for Hematologic Disorders. Cells. 2023;12(13):1755. doi: 10.3390/cells12131755.
  14. Huang Y, Chen L, Zhang Z, Liu Y, et al. Integration of histopathological image features and omics data in predicting molecular features and survival in glioblastoma. Front Med. 2025;12: 1510793. doi: 10.3389/fmed.2025.1510793.
  15. Bajčetić M, Mirčić A, Rakočević J, Đoković D, Milutinović K, Zaletel I. Comparing the performance of artificial intelligence learning models to medical students in solving histology and embryology multiple choice questions. Ann Anat. 2024;254:152261. doi: 10.1016/j.aanat.2024.152261.
  16. Pati P, Karkampouna S, Bonollo F, Compérat E, Radić M, et al. Accelerating histopathology workflows with generative AI-based virtually multiplexed tumour profiling. Nat Mach Intell. 2024; 6(9):1077-93. doi: 10.1038/s42256-024-00889-5.
  17. Li YX, Chen F, Shi JJ, Huang YL, Wang M. Convolutional Neural Networks for Classifying Cervical Cancer Types Using Histological Images. J Digit Imaging. 2023;36(2):441-9. doi: 10.1007/ s10278-022-00722-8.
  18. Greeley C, Holder L, Nilsson EE, Skinner MK. Scalable deep learning artificial intelligence slide analysis and validation. Sci Rep. 2024;14(1): 26748. doi: 10.1038/s41598-024-76807-x.
  19. Li J, Sarma KV, Chung HoK, Gertych A, Knudsen BS, Arnold CW. A Multi-scale U-Net for Semantic Segmentation of Histological Images from Radical Prostatectomies. AMIA Annu Symp Proc. 2018;2017:1140-8. PMID: 29854182.
  20. Kosaraju S, Park J, Lee H, Yang JW, Kang M. Deep learning-based framework for slide-based histopathological image analysis. Sci Rep. 2022; 12(1):19075. doi: 10.1038/s41598-022-23166-0.

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Published

2025-10-30

How to Cite

Aliyeva, O. ., Zviahina, H., Pototska, O., Makyeyeva, L., & Hromokovska, T. (2025). The use of artificial intelligence in medical morphology and medical education. Морфологія / Morphologia / Morfologìâ, 19(3), 191–195. https://doi.org/10.26641/1997-9665.2025.3.191-195

Issue

Vol. 19 No. 3 (2025)

Section

Статті

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