REVIEW PAPER
Artificial intelligence in pathology: from image analysis to clinical decision support
1
Student Research Group at the Chair and Department of Pathomorphology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia in Katowice, Poland
2
Dr. B. Hager Multi-Specialist District Hospital, Tarnowskie Gory, Poland
3
Chair and Department of Pathomorphology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia in Katowice, Poland
Submission date: 2025-02-09
Final revision date: 2025-05-26
Acceptance date: 2025-06-01
Online publication date: 2025-09-12
Publication date: 2026-06-29
Corresponding author
Jakub Fiegler-Rudol
Student Research Group at the Chair and Department of Pathomorphology Faculty of Medical Sciences Medical University of Silesia Zabrze, Poland
Student Research Group at the Chair and Department of Pathomorphology Faculty of Medical Sciences Medical University of Silesia Zabrze, Poland
Medical Studies 2026;42(2):131-139
KEYWORDS
TOPICS
ABSTRACT
Artificial intelligence is transforming pathology by enhancing diagnostic precision and supporting clinical decision-making. Its application often involves digital imaging and machine learning to analyse whole slide imaging (WSI) data. AI algorithms can detect cellular abnormalities, classify cancers, and predict outcomes more accurately than traditional approaches, enabling faster and more reliable diagnoses. This is especially valuable in cancer pathology, where early detection and precise classification are critical for effective treatment. Despite its benefits, AI faces challenges, including limited data availability, algorithm validation, and seamless integration into clinical workflows. This paper reviews current literature on AI’s role in pathology, highlighting its capacity to improve diagnostic accuracy and efficiency while examining barriers to broader adoption. As technology advances, AI holds substantial promise to revolutionize the discipline, ultimately enhancing patient care and reducing diagnostic errors. By addressing challenges, AI-driven pathology may soon become a cornerstone of modern healthcare.
REFERENCES (61)
1.
Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019; 380: 1347-1358.
2.
Cheng JY, Abel JT, Balis UGJ, McClintock DS, Pantanowitz L. Challenges in the development, deployment, and regulation of artificial intelligence in anatomic pathology. Am J Pathol. 2021; 191: 1684-1692.
3.
Nam S, Chong Y, Jung CK, Kwak TY, Lee JY, Park J, Rho MJ, Go H. Introduction to digital pathology and computer-aided pathology. J Pathol Transl Med. 2020; 54: 125-134.
4.
Patrzyk S, Woźniacka A. Sztuczna inteligencja w medycynie. Łódź: Uniwersytet Medyczny w Łodzi. UMedical Reports 2022; 6: 1-30.
5.
Jiang Y, Yang M, Wang S, Li X, Sun Y. Emerging role of deep learning-based artificial intelligence in tumor pathology. Cancer Commun (Lond). 2020; 40(4): 154-166.
6.
Shafi S, Parwani AV. Artificial intelligence in diagnostic pathology. Diagn Pathol. 2023; 18: 109.
7.
Stanford University Human-Centered Artificial Intelligence. Artificial Intelligence Definitions. https://www.stanford.edu/searc...=, [accessed 10.07.2024].
8.
Dechter R. Learning while searching in constraint-satisfaction problems. AAAI Conference on Artificial Intelligence 1986; 178-183.
9.
Bengtsson E, Malm P. Screening for cervical cancer using automated analysis of PAP-smears. Comput Math Methods Med. 2014; 2014: 842037.
10.
Evans AJ, Bauer TW, Bui MM, Cornish TC, Duncan H, Glassy EF, Hipp J, McGee RS, Murphy D, Myers C, O’Neill DG, Parwani AV, Rampy BA, Salama ME, Pantano- witz L. US Food and Drug Administration approval of whole slide imaging for primary diagnosis: a key milestone is reached and new questions are raised. Arch Pathol Lab Med. 2018; 142: 1383-1387.
11.
FDA. FDA Authorizes Software that Can Help Identify Prostate Cancer. https://www.fda.gov/news-event..., [accessed 17.04.2024].
12.
Niewęgłowski K, Wilczek N, Madoń B, Palmi J, Wasy- luk M. Zastosowania sztucznej inteligencji (AI) w medycynie. Med Og Nauk Zdr. 2021; 27: 213-219.
13.
Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA Cancer J Clin. 2024; 74: 12-49.
14.
Gandhi Z, Gurram P, Amgai B, Lekkala SP, Lokhandwala A, Manne S, Mohammed A, Koshiya H, Dewaswala N, Desai R, Bhopalwala H, Ganti S, Surani S. Artificial intelligence and lung cancer: impact on improving patient outcomes. Cancers (Basel). 2023; 15: 5236.
15.
Yang H, Chen L, Cheng Z, Yang M, Wang J, Lin C, Wang Y, Huang L, Chen Y, Peng S, Zunfu Ke Z, Wei- zhong Li W. Deep learning-based six-type classifier for lung cancer and mimics from histopathological whole slide images: a retrospective study. BMC Med. 2021; 19: 80.
16.
Shim WS, Yim K, Kim TJ, Sung YE, Lee G, Hong JH, Chun SH, Kim S, An HJ, Na SJ, Kim JJ, Moon MH, Moon SW, Park S, Hong SA, Ko YH. DeepRePath: identifying the prognostic features of early-stage lung adenocarcinoma using multi-scale pathology images and deep convolutional neural networks. Cancers (Basel). 2021; 13: 3308.
17.
Kim I, Kang K, Song Y, Kim TJ. Application of artificial intelligence in pathology: trends and challenges. Diagnostics (Basel). 2022; 12: 2794.
18.
Park S, Ock CY, Kim H, Pereira S, Park S, Ma M, Choi S, Kim S, Shin S, Aum BJ, Paeng K, Yoo D, Cha H, Park S, Suh KJ, Jung HA, Kim SH, Kim YJ, Sun JM, Chung JH, Ahn JS, Ahn MJ, Lee JS, Park K, Song SY, Bang YJ, Choi YL, Mok TS, Lee SH. Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes as complementary biomarker for immune checkpoint inhibition in non-small-cell lung cancer. J Clin Oncol. 2022; 40: 1916-1928.
19.
Gao Q, Yang L, Lu M, Jin R, Ye H, Ma T. The artificial intelligence and machine learning in lung cancer immunotherapy. J Hematol Oncol. 2023; 16: 55.
20.
Rakaee M, Adib E, Ricciuti B, Sholl LM, Shi W, Alessi JV, Cortellini A, Fulgenzi CAM, Viola P, Pinato DJ, Hashemi S, Bahce I, Houda I, Ulas EB, Radonic T, Väyrynen JP, Richardsen E, Jamaly S, Andersen S, Donnem T, Awad MM, Kwiatkowski DJ. Association of machine learning-based assessment of tumor-infiltrating lymphocytes on standard histologic images with outcomes of immunotherapy in patients with NSCLC. JAMA Oncol. 2023; 9: 51-60.
21.
Papalia GF, Brigato P, Sisca L, Maltese G, Faiella E, Santucci D, Pantano F, Vincenzi B, Tonini G, Papalia R, Denaro V. Artificial intelligence in detection, management, and prognosis of bone metastasis: a systematic review. Cancers (Basel). 2024; 16: 2700.
22.
Hunter B, Hindocha S, Lee RW. The role of artificial intelligence in early cancer diagnosis. Cancers (Basel). 2022; 14: 1524.
23.
Vandenberghe ME, Scott ML, Scorer PW, Soderberg M, Balcerzak D, Barker C. Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer. Sci Rep. 2017; 7: 45938.
24.
Bi WL, Hosny A, Schabath MB, Giger ML, Birkbak NJ, Mehrtash A, Allison T, Arnaout O, Abbosh C, Dunn IF, Mak RH, Tamimi RM, Tempany CM, Swanton C, Hoffmann U, Schwartz LH, Gillies RJ, Huang RY, Aerts HJWL. Artificial intelligence in cancer imaging: clinical challenges and applications. CA Cancer J Clin. 2019; 69: 127-157.
25.
Liu Y, Kohlberger T, Norouzi M, Dahl GE, Smith JL, Mohtashamian A, Olson N, Peng LH, Hipp JD, Stumpe MC. Artificial intelligence-based breast cancer nodal metastasis detection: insights into the black box for pathologists. Arch Pathol Lab Med. 2019; 143: 859-868.
26.
Bejnordi BE, Veta M, Johannes van Diest P, van Ginneken B, Karssemeijer N, Litjens G, van der Laak JAWM; the CAMELYON16 Consortium; Hermsen M, Manson QF, Balkenhol M, Geessink O, Stathonikos N, van Dijk MC, Bult P, Beca F, Beck AH, Wang D, Khosla A, Gargeya R, Irshad H, Zhong A, Dou Q, Li Q, Chen H, Lin HJ, Heng PA, Haß C, Bruni E, Wong Q, Halici U, Öner MU, Cetin-Atalay R, Berseth M, Khvatkov V, Vylegzhanin A, Kraus O, Shaban M, Rajpoot N, Awan R, Sirinukunwattana K, Qaiser T, Tsang YW, Tellez D, Annuscheit J, Hufnagl P, Valkonen M, Kartasalo K, Latonen L, Ruusuvuori P, Liimatainen K, Albarqouni S, Mungal B, George A, Demirci S, Navab N, Watanabe S, Seno S, Takenaka Y, Matsuda H, Phoulady HA, Kovalev V, Kalinovsky A, z Liauchuk V, Bueno G, Fernandez-Carrobles MM, Serrano I, Deniz O, Racoceanu D, Venâncio R. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA. 2017; 318: 2199-2210.
28.
Zeune LL, de Wit S, Berghuis AMS, IJzerman MJ, Terstappen LWMM, Brune C. How to agree on a CTC: evaluating the consensus in circulating tumor cell scoring. Cytometry A. 2018; 93: 1202-1206.
29.
Tzenios N, Tazanios ME, Chahine M. The impact of BMI on breast cancer – an updated systematic review and meta-analysis. Medicine (Baltimore). 2024; 103: e36831.
30.
Araújo T, Aresta G, Castro E, Rouco J, Aguiar P, Eloy C, Polónia A, Campilho A. Classification of breast cancer histology images using Convolutional Neural Networks. PLoS One. 2017; 12: e0177544.
31.
Wang D, Khosla A, Gargeya R, Irshad H, Beck A. Deep learning for identifying metastatic breast cancer. https://arxiv.org/pdf/1606.057....
32.
Ström P, Kartasalo K, Olsson H, Solorzano L, Delahunt B, Berney DM, Bostwick DG, Evans AJ, Grignon DJ, Humphrey PA, Iczkowski KA, Kench JG, Kristiansen G, van der Kwast TH, Leite KRM, McKenney JK, Oxley J, Pan CC, Samaratunga H, Srigley JR, Takahashi H, Tsuzuki T, Varma M, Zhou M, Lindberg J, Lindskog C, Ruusuvuori P, Wählby C, Grönberg H, Rantalainen M, Egevad L, Eklund M. Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study. Lancet Oncol. 2020; 21: 222-232.
33.
Soliman A, Li Z, Parwani AV. Artificial intelligence’s impact on breast cancer pathology: a literature review. Diagn Pathol. 2024; 19: 38.
34.
Dileep G, Gianchandani Gyani SG. Artificial intelligence in breast cancer screening and diagnosis. Cureus. 2022; 14: e30318.
35.
Ahn JS, Shin S, Yang SA, Park EK, Kim KH, Cho SI, Ock CY, Kim S. Artificial intelligence in breast cancer diagnosis and personalized medicine. J Breast Cancer. 2023; 26: 405-435.
36.
Bai S, Nasir S, Khan RA. Breast cancer diagnosis: a comprehensive exploration of explainable artificial intelligence (XAI) techniques. arXiv. preprint arXiv:2406.00532, 2024.
37.
Xu HL, Gong TT, Liu FH, Chen HY, Xiao Q, Hou Y, Huang Y, Sun HZ, Shi Y, Gao S, Lou Y, Chang Q, Zhao YH, Gao QL, Wu QJ. Artificial intelligence performance in image-based ovarian cancer identification: a systematic review and meta-analysis. EClinicalMedicine. 2022; 53: 101662.
38.
Breen J, Allen K, Zucker K, Adusumilli P, Scarsbrook A, Hall G, Orsi NM, Ravikumar N. Artificial intelligence in ovarian cancer histopathology: a systematic review. NPJ Precis Oncol. 2023; 7: 83.
39.
Akazawa M, Hashimoto K. Artificial intelligence in ovarian cancer diagnosis. Anticancer Res. 2020; 40: 4795-4800.
40.
Cheung AA, Rangaswamy S, Takahashi S. Literary review of artificial intelligence in ovarian cancer: transforming diagnosis, treatment, and future advancements/ OxJournal [Internet]. 2024 [cited 2024 Oct 25]. Available from: https://www.oxjournal.org/lite....
41.
Gandotra S, Kumar Y, Modi N, Choi J, Shafi J, Fazal M. Comprehensive analysis of artificial intelligence techniques for gynaecological cancer: symptoms identification, prognosis and prediction. Artif Intell Rev. 2024; 57: 220.
42.
Erdemoglu E, Serel TA, Karacan E, Köksal OK, Turan İ, Öztürk V, Bozkurt KK. Artificial intelligence for prediction of endometrial intraepithelial neoplasia and endometrial cancer risks in pre- and postmenopausal women. AJOG Glob Rep. 2023; 3: 100154.
43.
Butt SR, Soulat A, Lal PM, Fakhor H, Patel SK, Ali MB, Arwani S, Mohan A, Majumder K, Kumar V, Tejwaney U, Kumar S. Impact of artificial intelligence on the diagnosis, treatment and prognosis of endometrial cancer. Ann Med Surg (Lond). 2024; 86: 1531-1539.
44.
Riaz IB, Harmon S, Chen Z, Naqvi SAA, Cheng L. Applications of artificial intelligence in prostate cancer care: a path to enhanced efficiency and outcomes. Am Soc Clin Oncol Educ Book. 2024; 44: e438516.
45.
Chervenkov L, Sirakov N, Kostov G, Velikova T, Hadjidekov G. Future of prostate imaging: artificial intelligence in assessing prostatic magnetic resonance imaging. World J Radiol. 2023; 15: 136-145.
46.
Liu X, Shi J, Li Z, Huang Y, Zhang Z, Zhang C. The present and future of artificial intelligence in urological cancer. J Clin Med. 2023; 12: 4995.
47.
Mata L, Retamero J, Gupta R, García Figueras R, Luna A. Artificial intelligence-assisted prostate cancer diagnosis: radiologic-pathologic correlation. Radiographics. 2021; 41: 1676-1697.
48.
Bulten W, Balkenhol M, Belinga JA, Brilhante A, Çakır A, Egevad L, Eklund M, Farré X, Geronatsiou K, Molinié V, Pereira G, Roy P, Saile G, Salles P, Schaafsma E, Tschui J, Vos M. Artificial intelligence assistance significantly improves Gleason grading of prostate biopsies by pathologists. Mod Pathol. 2021; 34: 660-671.
49.
Zhang YN, Fowler KJ, Ozturk A, Potu CK, Louie AL, Montes V, Henderson WC, Wang K, Andre MP, Samir AE, Sirlin CB. Liver fibrosis imaging: a clinical review of ultrasound and magnetic resonance elastography. J Magn Reson Imaging. 2020; 51: 25-42.
50.
Standish RA, Cholongitas E, Dhillon A, Burroughs AK, Dhillon AP. An appraisal of the histopathological assessment of liver fibrosis. Gut. 2006; 55: 569-78.
51.
Abdullah R, Fakieh B. Health care employees’ perceptions of the use of artificial intelligence applications: survey study. J Med Internet Res. 2020; 22: e17620.
52.
Popa SL, Ismaiel A, Abenavoli L, Padureanu AM, Dita MO, Bolchis R, Munteanu MA, Brata VD, Pop C, Bosneag A, Dumitrascu DI, Barsan M, David L. Diagnosis of liver fibrosis using artificial intelligence: a systematic review. Medicina (Kaunas). 2023; 59: 992.
53.
Popa SL, Grad S, Chiarioni G, Masier A, Peserico G, Bra- ta VD, Dumitrascu DI, Fantin A. Applications of artificial intelligence in the automatic diagnosis of focal liver lesions: a systematic review. J Gastrointestin Liver Dis. 2023; 32: 77-85..
54.
Badam RK, Sownetha T, Babu DBG, Waghray S, Reddy L, Garlapati K, Chavva S. Virtopsy. Virtopsy: touch-free autopsy. J Forensic Dent Sci. 2017; 9: 42.
55.
O’Sullivan S, Holzinger A, Wichmann D, Saldiva PHN, Sajid MI, Zatloukal K. Virtual autopsy: machine learning and AI provide new opportunities for investigating minimal tumor burden and therapy resistance by cancer patients. Autops Case Rep. 2018; 8: e2018003.
56.
Maleki Varnosfaderani S, Forouzanfar M. The role of AI in hospitals and clinics: transforming healthcare in the 21st century. Bioengineering (Basel). 2024; 11: 337.
57.
Visibelli A, Roncaglia B, Spiga O, Santucci A. The impact of artificial intelligence in the odyssey of rare diseases. Biomedicines. 2023; 11: 887.
58.
Reis-Filho JS, Kather JN. Overcoming the challenges to implementation of artificial intelligence in pathology. J Natl Cancer Inst. 2023; 115: 608-612.
59.
Kiran N, Sapna F, Kiran F, Kumar D, Raja F, Shiwlani S, Paladini A, Sonam F, Bendari A, Perkash RS, Anjali F, Varrassi G. Digital pathology: transforming diagnosis in the digital age. Cureus. 2023; 15: e44620.
60.
Johnson D, Goodman R, Patrinely J, Stone C, Zimmerman E, Donald R, Chang S, Berkowitz S, Finn A, Jahan- gir E, Scoville E, Reese T, Friedman D, Bastarache J, van der Heijden Y, Wright J, Carter N, Alexander M, Choe J, Chastain C, Zic J, Horst S, Turker I, Agarwal R, Osmundson E, Idrees K, Kieman C, Padmanabhan C, Bailey C, Schlegel C, Chambless L, Gibson M, Osterman T, Whe- less L. Assessing the accuracy and reliability of AI-generated medical responses: an evaluation of the Chat-GPT model. Res Sq. 2023: rs.3.rs-2566942.
61.
Nam D, Chapiro J, Paradis V, Seraphin TP, Kather JN. Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction. JHEP Rep 2022; 4: 100443.
Share
RELATED ARTICLE
| eISSN: | 2300-6722 |
| ISSN: | 1899-1874 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
