The site of the Media Sphera Publishers contains materials intended solely for healthcare professionals.
By closing this message, you confirm that you are a certified medical professional or a student of a medical educational institution.

Login
EN
+7 (495) 482-4118
+7 (495) 482-4329
+8 800 250-18-12
  • (toll-free number for subscriptions)
    Mon-Fri from 10 a.m. to 6 p.m.

  • © 1998-2026
+7 (495) 482-43-29
EN
All journals
Journal
Year
Year
Search result: 0

Solovev I.A.

Pitirim Sorokin Syktyvkar State University

Artificial intelligence in pathological anatomy

Authors:

Solovev I.A.

More about the authors

Journal: Russian Journal of Archive of Pathology. 2024;86(2): 65‑71

DOI: 10.17116/patol20248602165

Read: 3087 times

Download PDF (RU)
Contact the author
Table of contents

ARTIFICIAL INTELLIGENCE IN PATHOLOGICAL ANATOMY
ARTIFICIAL INTELLIGENCE IN PATHOLOGICAL ANATOMY

To cite this article:

Solovev IA. Artificial intelligence in pathological anatomy. Russian Journal of Archive of Pathology. 2024;86(2):65‑71. (In Russ.)
https://doi.org/10.17116/patol20248602165

Подписка 2026 Архив патологии (Russian Journal of Archive of Pathology)
МСФ на ЯМ
Использование инфографики авторами научных работ
Close metadata
Recommended articles:
Liver pathology in COVID-19. Russian Journal of Archive of Pathology. 2025;(1):53-59
Infe­ctious diseases of the respiratory system in Irku­tsk in 2023 acco­rding to auto­psy data. Russian Journal of Archive of Pathology. 2025;(2):37-42
Arti­ficial Inte­lligence in Dentistry: A Sign of the Times. Stomatology. 2025;(1):87-92
Arti­ficial inte­lligence-based software for digi­tal asse­ssment of repa­rative bone tissue rege­neration. Rege­nerative Biotechnologies, Preventive, Digi­tal and Predictive Medi­cine. 2025;(1):19-24
On the path to personalized cardiology: prospects of precise asse­ssment of cardiovascular risks and predictive-prognostic diagnostics. Russian Journal of Cardiology and Cardiovascular Surgery. 2025;(2):206-215
Arti­ficial inte­lligence in evaluating reju­venation effect after periorbital endo­scopic surgery. Plastic Surgery and Aesthetic Medi­cine. 2025;(2):42-51
Application of neural networks for improving the methods of asse­ssment of corneal nerve fibers (preliminary report). Russian Annals of Ophthalmology. 2025;(2):117-122
Arti­ficial inte­lligence in asse­ssment of indi­vidual risks of age-related macu­lar dege­neration progression. Russian Annals of Ophthalmology. 2025;(2):123-128
Prognostic model and calculator for asse­ssing the risk of prolonged pleural effu­sion after lobe­ctomy. Piro­gov Russian Journal of Surgery. 2025;(6):26-34
Arti­ficial inte­lligence capa­bilities in multiple scle­rosis. S.S. Korsakov Journal of Neurology and Psychiatry. 2025;(5):14-21
Abstract:

The review presents key concepts and global developments in the field of artificial intelligence used in pathological anatomy. The work examines two types of artificial intelligence (AI): weak and strong ones. A review of experimental algorithms using both deep machine learning and computer vision technologies to work with WSI images of preparations, diagnose and make a prognosis for various malignant neoplasms is carried out. It has been established that weak artificial intelligence at this stage of development of computer (digital) pathological anatomy shows significantly better results in speeding up and refining diagnostic procedures than strong artificial intelligence having signs of general intelligence. The article also discusses three options for the further development of AI assistants for pathologists based on the technologies of large language models (strong AI) ChatGPT (PathAsst), Flan-PaLM2 and LIMA. As a result of the analysis of the literature, key problems in the field were identified: the equipment of pathology institutions, the lack of experts in training neural networks, the lack of strict criteria for the clinical viability of AI diagnostic technologies.

Keywords:

WSI analysis
pathological anatomy
AI pathologist’s assistant
deep machine learning
neural networks
artificial intelligence

Authors:

Solovev I.A.

Pitirim Sorokin Syktyvkar State University

Received:

30.10.2023

Accepted:

27.12.2023

Close metadata

References:

  1. Al-Rifaie MM, Bishop M. Weak and strong computational creativity. Ch. In: Besold T, Schorlemmer M, Smaill A, eds. Computational creativity research: towards creative machines. Atlantis Thinking Machines. Vol. 7. Paris: Atlantis Press; 2014;37-49.  https://doi.org/10.2991/978-94-6239-085-0_2
  2. Meyer J, Khademi A, Têtu B, Han W, Nippak P, Remisch D. Impact of artificial intelligence on pathologists’ decisions: an experiment. J Am Med Inform Assoc. 2022;29(10):1688-1695. https://doi.org/10.1093/jamia/ocac103
  3. Nakagawa K, Moukheiber L, Celi LA, Patel M, Mahmood F, Gondim D, Hogarth M, Levenson R. AI in pathology: what could possibly go wrong? Semin Diagn Pathol. 2023;40(2):100-108.  https://doi.org/10.1053/j.semdp.2023.02.006
  4. Försch S, Klauschen F, Hufnagl P, Roth W. Artificial intelligence in pathology. Dtsch Arztebl Int. 2021;118(12):194-204.  https://doi.org/10.3238/arztebl.m2021.0011
  5. Drogt J, Milota M, Vos S, Bredenoord A, Jongsma K. Integrating artificial intelligence in pathology: a qualitative interview study of users’ experiences and expectations. Mod Pathol. 2022;35(11): 1540-1550. https://doi.org/10.1038/s41379-022-01123-6
  6. Cooper MC, Ji Z, Krishnan RG. Machine learning in computational histopathology: challenges and opportunities. Genes Chromosomes Cancer. 2023;62(9):540-556.  https://doi.org/10.1002/gcc.23177
  7. Cifci D, Veldhuizen G, Foersch S, Kather JN. AI in computational pathology of cancer: improving diagnostic workflows and clinical outcomes? Ann Rev Cancer Biol. 2023;7(1):57-71.  https://doi.org/10.1146/annurev-cancerbio-061521-092038
  8. Wang S, Yang DM, Rong R, Zhan X, Xiao G. Pathology image analysis using segmentation deep learning algorithms. Am J Pathol. 2019;189(9):1686-1698. https://doi.org/10.1016/j.ajpath.2019.05.007
  9. Ghaffari Laleh N, Truhn D, Veldhuizen GP, Han T, van Treeck M, Buelow RD, Langer R, Dislich B, Boor P, Schulz V, et al. Adversarial attacks and adversarial robustness in computational pathology. Nat Commun. 2022;13(1):5711. https://doi.org/10.1038/s41467-022-33266-0
  10. Kim I, Kang K, Song Y, Kim TJ. Application of artificial intelligence in pathology: trends and challenges. Diagnostics. 2022; 12(11):2794. https://doi.org/10.3390/diagnostics12112794
  11. Zhang Z, Chen P, McGough M, Xing F, Wang C, Bui M, Xie Y, Sapkota M, Cui L, Dhillon J, et al. Pathologist-level interpretable whole-slide cancer diagnosis with deep learning. Nat Mach Intell. 2019;1:236-245.  https://doi.org/10.1038/s42256-019-0052-1
  12. Khoraminia F, Fuster S, Kanwal N, Olislagers M, Engan K, van Leenders GJLH, Stubbs AP, Akram F, Zuiverloon TCM. Artificial intelligence in digital pathology for bladder cancer: hype or hope? A systematic review. Cancers (Basel). 2023;15(18):4518. https://doi.org/10.3390/cancers15184518
  13. Froń A, Semianiuk A, Lazuk U, Ptaszkowski K, Siennicka A, Lemiński A, Krajewski W, Szydełko T, Małkiewicz B. Artificial intelligence in urooncology: what we have and what we expect. Cancers (Basel). 2023;15(17):4282. https://doi.org/10.3390/cancers15174282
  14. Baxi V, Edwards R, Montalto M, Saha S. Digital pathology and artificial intelligence in translational medicine and clinical practice. Mod Pathol. 2021;35(1):23-32.  https://doi.org/10.1038/s41379-021-00919-2
  15. Jain DK, Lakshmi KM, Varma KP, Ramachandran M, Bharati S. Lung cancer detection based on Kernel PCA-convolution neural network feature extraction and classification by fast deep belief neural network in disease management using multimedia data sources. Comput Intell Neurosci. 2022;2022:3149406. https://doi.org/10.1155/2022/3149406
  16. Tsuneki M, Kanavati F. Weakly supervised learning for multi-organ adenocarcinoma classification in whole slide images. PLoS One. 2022;17(11):e0275378. https://doi.org/10.1371/journal.pone.0275378
  17. Civit-Masot J, Bañuls-Beaterio A, Domínguez-Morales M, Rivas-Pérez M, Muñoz-Saavedra L, Rodríguez Corral JM. Non-small cell lung cancer diagnosis aid with histopathological images using Explainable Deep Learning techniques. Comput Methods Programs Biomed. 2022;226:107108. https://doi.org/10.1016/j.cmpb.2022.107108
  18. Shvetsov N, Grønnesby M, Pedersen E, Møllersen K, Busund LR, Schwienbacher R, Bongo LA, Kilvaer TK. A pragmatic machine learning approach to quantify tumor-infiltrating lymphocytes in whole slide images. Cancers (Basel). 2022;14(12):2974. https://doi.org/10.3390/cancers14122974
  19. Qaiser T, Mukherjee A, Reddy Pb C, Munugoti SD, Tallam V, Pitkäaho T, Lehtimäki T, Naughton T, Berseth M, Pedraza A, et al. HER2 challenge contest: a detailed assessment of automated HER2 scoring algorithms in whole slide images of breast cancer tissues. Histopathology. 2018;72(2):227-238.  https://doi.org/10.1111/his.13333
  20. Cruz-Roa A, Gilmore H, Basavanhally A, Feldman M, Ganesan S, Shih NNC, Tomaszewski J, González FA, Madabhushi A. Accurate and reproducible invasive breast cancer detection in whole-slide images: a Deep Learning approach for quantifying tumor extent. Sci Rep. 2017;7:46450. https://doi.org/10.1038/srep46450
  21. Im S, Hyeon J, Rha E, Lee J, Choi HJ, Jung Y, Kim TJ. Classification of diffuse glioma subtype from clinical-grade pathological images using deep transfer learning. Sensors (Basel). 2021;21(10):3500. https://doi.org/10.3390/s21103500
  22. Pei L, Jones KA, Shboul ZA, Chen JY, Iftekharuddin KM. Deep neural network analysis of pathology images with integrated molecular data for enhanced glioma classification and grading. Front Oncol. 2021;11:668694. https://doi.org/10.3389/fonc.2021.668694
  23. Khalsa SSS, Hollon TC, Adapa A, Urias E, Srinivasan S, Jairath N, Szczepanski J, Ouillette P, Camelo-Piragua S, Orringer DA. Automated histologic diagnosis of CNS tumors with machine learning. CNS Oncol. 2020;9(2):CNS56. https://doi.org/10.2217/cns-2020-0003
  24. Zhou C, Jin Y, Chen Y, Huang S, Huang R, Wang Y, Zhao Y, Chen Y, Guo L, Liao J. Histopathology classification and localization of colorectal cancer using global labels by weakly supervised deep learning. Comput Med Imaging Graph. 2021;88:101861. https://doi.org/10.1016/j.compmedimag.2021.101861
  25. Wang KS, Yu G, Xu C, Meng XH, Zhou J, Zheng C, Deng Z, Shang L, Liu R, Su S, et al. Accurate diagnosis of colorectal cancer based on histopathology images using artificial intelligence. BMC Med. 2021;19(1):76.  https://doi.org/10.1186/s12916-021-01942-5
  26. Dabass M, Vashisth S, Vig R. A convolution neural network with multi-level convolutional and attention learning for classification of cancer grades and tissue structures in colon histopathological images. Comput Biol Med. 2022;147:105680. https://doi.org/10.1016/j.compbiomed.2022.105680
  27. Ba W, Wang S, Shang M, Zhang Z, Wu H, Yu C, Xing R, Wang W, Wang L, Liu C, et al. Assessment of deep learning assistance for the pathological diagnosis of gastric cancer. Mod Pathol. 2022; 35(9):1262-1268. https://doi.org/10.1038/s41379-022-01073-z
  28. Wang SZ, Wang JG, Lu Y, Zhang YJ, Xin FJ, Liu SL, Zhang XX, Liu GW, Li S, Sui D, Clinical application of convolutional neural network in pathological diagnosis of metastatic lymph nodes of gastric cancer. Zhonghua Wai Ke Za Zhi. 2019;57(12):934-938.  https://doi.org/10.3760/cma.j.issn.0529-5815.2019.12.012
  29. Bertsimas D, Margonis GA, Tang S, Koulouras A, Antonescu CR, Brennan MF, Martin-Broto J, Rutkowski P, Stasinos G, Wang J, et al. An interpretable AI model for recurrence prediction after surgery in gastrointestinal stromal tumour: an observational cohort study. eClinicalMedicine. 2023;64:102200. https://doi.org/10.1016/j.eclinm.2023.102200
  30. Shen M, Zou Z, Bao, Fairley CK, Canfell K, Ong JJ, Hocking J, Chow EPF, Zhuang G, Wang L, et al. Cost-effectiveness of artificial intelligence-assisted liquid-based cytology testing for cervical cancer screening in China. Lancet Reg Health West Pac. 2023;34:100726. https://doi.org/10.1016/j.lanwpc.2023.100726
  31. Shiga T, Taguchi A, Mori M, Yamaguchi S, Honjoh H, Nishijima A, Eguchi S, Miyamoto Y, Sone K, Kawana K, et al. Risk stratification of invasive cervical cancer diagnosed after cervical conization. Jpn J Clin Oncol. 2023;53(12):1138-1143. https://doi.org/10.1093/jjco/hyad121
  32. Alsalatie M, Alquran H, Mustafa WA, Zyout A, Alqudah AM, Kaifi R, Qudsieh S. A new weighted deep learning feature using particle swarm and ant lion optimization for cervical cancer diagnosis on pap smear images. Diagnostics (Basel). 2023;13(17):2762. https://doi.org/10.3390/diagnostics13172762
  33. Yu H, Luo S, Ji J, Wang Z, Zhi W, Mo N, Zhong P, He C, Wan T, Jin Y. A deep-learning-based artificial intelligence system for the pathology diagnosis of uterine smooth muscle tumor. Life (Basel). 2022;13(1):3.  https://doi.org/10.3390/life13010003
  34. Deng C, Li D, Feng M, Han D, Huang Q. The value of deep neural networks in the pathological classification of thyroid tumors. Diagn Pathol. 2023;18(1):95.  https://doi.org/10.1186/s13000-023-01380-2
  35. Nagendra L, Pappachan JM, Fernandez CJ. Artificial intelligence in the diagnosis of thyroid cancer: recent advances and future directions. Artif Intell Cancer. 2023;4(1):1-10.  https://doi.org/10.35713/aic.v4.i1.1
  36. Painuli D, Bhardwaj S, Köse U. Recent advancement in cancer diagnosis using machine learning and deep learning techniques: a comprehensive review. Comput Biol Med. 2022;146:105580. https://doi.org/10.1016/j.compbiomed.2022.105580
  37. Busby D, Grauer R, Pandav K, Khosla A, Jain P, Menon M, Haines GK 3rd, Cordon-Cardo C, Gorin MA, Tewari AK. Applications of artificial intelligence in prostate cancer histopathology. Urol Oncol. 2023;S1078-1439(22)00487-2.  https://doi.org/10.1016/j.urolonc.2022.12.002
  38. Boellaard R, Wesseling D, Eertink C, de Vries B, Lugtenburg E, Zwezerijnen B, Wiegers S, Vet R, Golla S, Zijlstra J. Artificial Intelligence based outcome classification from baseline F-18-FDG PET/CT in de novo diffuse Large B-cell lymphoma patients. Eur J Nucl Med Mol Imaging. 2021;48(suppl 1):S348-S348.
  39. Li D, Bledsoe JR, Zeng Y, Li D, Bledsoe JR, Zeng Y, Liu W, Hu Y, Bi K, Liang A, et al. A deep learning diagnostic platform for diffuse large B-cell lymphoma with high accuracy across multiple hospitals. Nat Commun. 2020;11(1):6004. https://doi.org/10.1038/s41467-020-19817-3
  40. Steinbuss G, Kriegsmann M, Zgorzelski C, Brobeil A, Goeppert B, Dietrich S, Mechtersheimer G, Kriegsmann K. Deep learning for the classification of Non-Hodgkin lymphoma on histopathological images. Cancers (Basel). 2021;13(10):2419. https://doi.org/10.3390/cancers13102419
  41. Irshaid L, Bleiberg J, Weinberger E, Garritano J, Shallis RM, Patsenker J, Lindenbaum O, Kluger Y, Katz SG, Xu ML. Histopathologic and machine deep learning criteria to predict lymphoma transformation in bone marrow biopsies. Arch Pathol Lab Med. 2022;146(2):182-193.  https://doi.org/10.5858/arpa.2020-0510-OA
  42. Miyoshi H, Sato K, Kabeya Y, Yonezawa S, Nakano H, Takeuchi Y, Ozawa I, Higo S, Yanagida E, Yamada K, et al. Deep learning shows the capability of high-level computer-aided diagnosis in malignant lymphoma. Lab Invest. 2020;100(10):1300-1310. https://doi.org/10.1038/s41374-020-0442-3
  43. Achi HE, Belousova T, Chen L, Wahed A, Wang I, Hu Z, Kanaan Z, Rios A, Nguyen AND. Automated diagnosis of ymphoma with digital pathology images using deep learning. Ann Clin Lab Sci. 2019;49(2):153-160. 
  44. Mallesh N, Zhao M, Meintker L, Höllein A, Elsner F, Lüling H, Haferlach T, Kern W, Westermann J, Brossart P, et al. Knowledge transfer to enhance the performance of deep learning models for automated classification of B cell neoplasms. Patterns (N Y). 2021;2(10):100351. https://doi.org/10.1016/j.patter.2021.100351
  45. Doeleman T, Hondelink LM, Vermeer MH, van Dijk MR, Schrader AMR. Artificial intelligence in digital pathology of cutaneous lymphomas: a review of the current state and future perspectives. Semin Cancer Biol. 2023;94:81-88.  https://doi.org/10.1016/j.semcancer.2023.06.004
  46. Khan A, Brouwer N, Blank A, Müller F, Soldini D, Noske A, Gaus E, Brandt S, Nagtegaal I, Dawson H, et al. Computer-assisted diagnosis of lymph node metastases in colorectal cancers using transfer learning with an ensemble model. Mod Pathol. 2023; 36(5):100118. https://doi.org/10.1016/j.modpat.2023.100118
  47. Kiehl L, Kuntz S, Höhn J, Jutzi T, Krieghoff-Henning E, Kather JN, Holland-Letz T, Kopp-Schneider A, Chang-Claude J, Brobeil A, et al. Deep learning can predict lymph node status directly from histology in colorectal cancer. Eur J Cancer. 2021;157:464-473.  https://doi.org/10.1016/j.ejca.2021.08.039
  48. Ehteshami Bejnordi B, Veta M, Johannes van Diest P, van Ginneken B, Karssemeijer N, Litjens G, van der Laak JAWM; the CAMELYON16 Consortium; et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA. 2017;318(22):2199. https://doi.org/10.1001/jama.2017.14585
  49. Caldonazzi N, Rizzo PC, Eccher A, Girolami I, Fanelli GN, Naccarato AG, Bonizzi G, Fusco N, d’Amati G, Scarpa A, et al. Value of artificial intelligence in evaluating lymph node metastases. Cancers (Basel). 2023;15(9):2491. https://doi.org/10.3390/cancers15092491
  50. Sounderajah V, Ashrafian H, Golub RM, Shetty S, De Fauw J, Hooft L, Moons K, Collins G, Moher D, Bossuyt PM, et al.; STARD-AI Steering Committee. Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol. BMJ Open. 2021;11(6):e047709. https://doi.org/10.1136/bmjopen-2020-047709
  51. Giarnieri E, Scardapane S. Towards artificial intelligence applications in next generation cytopathology. Biomedicines. 2023; 11(8):2225. https://doi.org/10.3390/biomedicines11082225
  52. Chowdhery A, Narang S, Devlin J, Bosma M, Mishra G, Roberts A, Barham P, Chung HW, Sutton C, Gehrmann S, et al. PaLM: scaling language modeling with pathways. arXiv:220402311. 2022. https://arxiv.org/abs/2204.02311
  53. Nori H, King N, McKinney SM, Carignan D, Horvitz E. Capabilities of GPT-4 on medical challenge problems. arXiv:2303.13375. 2023. https://arxiv.org/abs/2303.13375
  54. Singhal K, Azizi S, Tu T, Mahdavi S, Wei J, Chung HW, Scales N, Tanwani A, Cole-Lewis H, Pfohl S, et al. Large language models encode clinical knowledge. Nature. 2023;620:172-180.  https://doi.org/10.1038/s41586-023-06291-2
  55. Sun Y, Zhu C, Zheng S, Zhang K, Shui Z, Yu X, Zhao Y, Li H, Zhang Y, Zhao R, et al. PathAsst: redefining pathology through generative foundation AI assistant for pathology. arXiv:2305.15072. 2023. Accessed: September 28, 2023. https://arxiv.org/abs/2305.15072
  56. Schukow C, Smith SC, Landgrebe E, Parasuraman S, Folaranmi OO, Paner GP, Amin MB. Application of ChatGPT in routine diagnostic pathology: promises, pitfalls, and potential future directions. Adv Anat Pathol. 2024;31(1):15-21.  https://doi.org/10.1097/pap.0000000000000406
  57. Qu L, Luo X, Fu K, Wang M, Song Z. The rise of AI language pathologists: exploring two-level prompt learning for few-shot weakly-supervised whole slide image classification. arXiv:2305.17891v1. 2023. https://arxiv.org/pdf/2305.17891v1.pdf
  58. Campanella G, Hanna MG, Geneslaw L, Miraflor A, Werneck Krauss Silva V, Busam KJ, Brogi E, Reuter VE, Klimstra DS, Fuchs TJ. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat Med. 2019;25(8): 1301-1309. https://doi.org/10.1038/s41591-019-0508-1
  59. Berbís MA, McClintock DS, Bychkov A, Van der Laak J, Pantanowitz L, Lennerz JK, Cheng JY, Delahunt B, Egevad L, Eloy C, et al. Computational pathology in 2030: a Delphi study forecasting the role of AI in pathology within the next decade. eBioMedicine. 2023;88:104427. https://doi.org/10.1016/j.ebiom.2022.104427
Contact the author
Email
Message
The publishing house "Media Sphere" does not provide treatment services, does not give recommendations on contacting medical institutions and specific specialists, on the prescription of medicines and dietary supplements.

Email Confirmation

An email was sent to test@gmail.com with a confirmation link. Follow the link from the letter to complete the registration on the site.

Email Confirmation

Contact us
If you have any questions, complaints or suggestions, please use this feedback form.
Email
Message
The publishing house "Media Sphere" does not provide treatment services, does not give recommendations on contacting medical institutions and specific specialists, on the prescription of medicines and dietary supplements.
Submit Application

You can become an author of one of our magazines, send a request and we will consider it in during the day.

Name
Phone
E-mail
Message
Login
Registration
E-mail
Password
Forgot Password?

Log in to the site using your account in one of the services

Password recovery
E-mail
Login
Register

We use cооkies to improve the performance of the site. By staying on our site, you agree to the terms of use of cооkies. To view our Privacy and Cookie Policy, please. click here.