Artificial intelligence (AI) and machine learning (ML) are rapidly advancing fields within computer science, driving significant progress in cancer diagnostics. Various ML models have been developed to assist diagnosis, guide therapy decisions, and facilitate early disease detection. In this review, we discuss diverse AI and ML approaches and critically evaluate their applications and limitations in pancreatic cancer histopathology, diagnostics, and biomarker discovery.

Center for Precision Medicine University Hospital Brno Brno Czech Republic

Department of Biostatistics St Anne's University Hospital Brno Brno Czech Republic

Department of Histology and Embryology Faculty of Medicine Masaryk University Kamenice 3 Brno 625 00 Czech Republic

Department of Internal Medicine Hematology and Oncology University Hospital Brno Brno Czech Republic

Department of Pathology University Hospital Brno Brno Czech Republic

International Clinical Research Center St Anne's University Hospital Brno Brno Czech Republic

Surgery Clinic University Hospital Brno Brno Czech Republic

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van der Laak J, Litjens G, Ciompi F. Deep learning in histopathology: the path to the clinic. Nat Med. 2021;27(5):775–84. 10.1038/s41591-021-01343-4. PubMed

Försch S, Klauschen F, Hufnagl P, Roth W. Artificial intelligence in pathology. Dtsch Arzteblatt Int. 2021;118(12):194–204. 10.3238/arztebl.m2021.0011. PubMed PMC

Burzykowski T, Rousseau A-J, Geubbelmans M, Valkenborg D. Introduction to machine learning. Am J Orthod Dentofac Orthop. 2023;163(5):732–4. 10.1016/j.ajodo.2023.02.005. PubMed

Salvi M, Acharya UR, Molinari F, Meiburger KM. The impact of Pre- and Post-Image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis. Comput Biol Med. 2021;128:104129. 10.1016/j.compbiomed.2020.104129. PubMed

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. 10.1038/s41598-022-23166-0. PubMed PMC

Mun SK, Wong KH, Lo S-CB, Li Y, Bayarsaikhan S. Artificial Intelligence for the Future Radiology Diagnostic Service. PubMed PMC

Haeberle L, Esposito I. Pathology of pancreatic Cancer. Transl Gastroenterol Hepatol. 2019;4:50. 10.21037/tgh.2019.06.02. PubMed PMC

Groot VP, Rezaee N, Wu W, Cameron JL, Fishman EK, Hruban RH, Weiss MJ, Zheng L, Wolfgang CL, He J, Patterns. Timing, and predictors of recurrence following pancreatectomy for pancreatic ductal adenocarcinoma. Ann Surg. 2018;267(5):936–45. 10.1097/SLA.0000000000002234. PubMed

Sántha P, Lenggenhager D, Finstadsveen A, Dorg L, Tøndel K, Amrutkar M, Gladhaug IP, Verbeke C. Morphological heterogeneity in pancreatic Cancer reflects structural and functional divergence. Cancers. 2021;13(4):895. 10.3390/cancers13040895. PubMed PMC

Verbeke C. Morphological heterogeneity in ductal adenocarcinoma of the Pancreas–. Does It Matter? Pancreatology. 2016;16(3):295–301. 10.1016/j.pan.2016.02.004. PubMed

Varadhachary GR, Tamm EP, Abbruzzese JL, Xiong HQ, Crane CH, Wang H, Lee JE, Pisters PWT, Evans DB, Wolff RA. Borderline resectable pancreatic cancer: definitions, management, and role of preoperative therapy. Ann Surg Oncol. 2006;13(8):1035–46. 10.1245/ASO.2006.08.011. PubMed

Garajová I, Peroni M, Gelsomino F, Leonardi FA. Simple overview of pancreatic Cancer treatment for clinical oncologists. Curr Oncol. 2023;30(11):9587–601. 10.3390/curroncol30110694. PubMed PMC

Halbrook CJ, Lyssiotis CA, Magliano MP di;, Maitra A. Pancreatic Cancer: Advances and Challenges. PubMed PMC

Krishna V, Tiu E, Krishna V, Vrabac D, Shah K, Abuzeid W, Smith K, Davelaar J, Nuesca C, Larson BK, Fountzilas C, Rajpurkar P, Hendifar AE, Collisson EA, Joshi A, Singhi AD. Development of artificial Intelligence–Derived histological biomarkers for First-Line treatment selection in metastatic pancreatic ductal adenocarcinoma (mPDAC). J Clin Oncol. 2023;41(4suppl):743–743. 10.1200/JCO.2023.41.4_suppl.743.

Elbanna KY, Jang H-J, Kim TK. Imaging diagnosis and staging of pancreatic ductal adenocarcinoma: A comprehensive review. Insights Imaging. 2020;11:58. 10.1186/s13244-020-00861-y. PubMed PMC

Park J, Lim F, Prest M, Ferris JS, Aziz Z, Agyekum A, Wagner S, Gulati R, Hur C. Quantifying the potential benefits of early detection for pancreatic Cancer through a counterfactual simulation modeling analysis. Sci Rep. 2023;13(1):20028. 10.1038/s41598-023-46751-3. PubMed PMC

Huang B, Huang H, Zhang S, Zhang D, Shi Q, Liu J, Guo J. Artificial intelligence in pancreatic Cancer. Theranostics. 2022;12(16):6931–54. 10.7150/thno.77949. PubMed PMC

Liu W, Zhang B, Liu T, Jiang J, Liu Y. Artificial intelligence in pancreatic image analysis: A review. Sensors. 2024;24(14):4749. 10.3390/s24144749. PubMed PMC

Hameed BS, Krishnan UM. Artificial Intelligence-Driven diagnosis of pancreatic Cancer. Cancers. 2022;14(21):5382. 10.3390/cancers14215382. PubMed PMC

Schuurmans M, Alves N, Vendittelli P, Huisman H, Hermans J. Setting the research agenda for clinical artificial intelligence in pancreatic adenocarcinoma imaging. Cancers. 2022;14(14):3498. 10.3390/cancers14143498. PubMed PMC

Daher H, Punchayil SA, Ismail AAE, Fernandes RR, Jacob J, Algazzar MH, Mansour M. Advancements in pancreatic Cancer detection: integrating biomarkers, imaging technologies, and machine learning for early diagnosis. Cureus PubMed PMC

Greener JG, Kandathil SM, Moffat L, Jones DT. A guide to machine learning for biologists. Nat Rev Mol Cell Biol. 2022;23(1):40–55. 10.1038/s41580-021-00407-0. PubMed

LeCun Y, Bengio Y, Hinton G, Deep Learning. Nature. 2015;521(7553):436–44. 10.1038/nature14539. PubMed

Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, Cui C, Corrado G, Thrun S, Dean J. A guide to deep learning in healthcare. Nat Med. 2019;25(1):24–9. 10.1038/s41591-018-0316-z. PubMed

Liu J, Fu F. Convolutional neural network model by deep learning and teaching robot in keyboard musical instrument teaching. PLoS ONE. 2023;18(10):e0293411. 10.1371/journal.pone.0293411. PubMed PMC

Janowczyk A, Madabhushi A. Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases. J Pathol Inf. 2016;7(1):29. 10.4103/2153-3539.186902. PubMed PMC

Cao K, Xia Y, Yao J, Han X, Lambert L, Zhang T, Tang W, Jin G, Jiang H, Fang X, Nogues I, Li X, Guo W, Wang Y, Fang W, Qiu M, Hou Y, Kovarnik T, Vocka M, Lu Y, Chen Y, Chen X, Liu Z, Zhou J, Xie C, Zhang R, Lu H, Hager GD, Yuille AL, Lu L, Shao C, Shi Y, Zhang Q, Liang T, Zhang L, Lu J. Large-Scale pancreatic Cancer detection via Non-Contrast CT and deep learning. Nat Med. 2023;29(12):3033–43. 10.1038/s41591-023-02640-w. PubMed PMC

Placido D, Yuan B, Hjaltelin JX, Zheng C, Haue AD, Chmura PJ, Yuan C, Kim J, Umeton R, Antell G, Chowdhury A, Franz A, Brais L, Andrews E, Marks DS, Regev A, Ayandeh S, Brophy MT, Do NV, Kraft P, Wolpin BM, Rosenthal MH, Fillmore NR, Brunak S, Sander C. A deep learning algorithm to predict risk of pancreatic Cancer from disease trajectories. Nat Med. 2023;29(5):1113–22. 10.1038/s41591-023-02332-5. PubMed PMC

Fu H, Mi W, Pan B, Guo Y, Li J, Xu R, Zheng J, Zou C, Zhang T, Liang Z, Zou J, Zou H. Automatic pancreatic ductal adenocarcinoma detection in whole slide images using deep convolutional neural networks. Front Oncol. 2021;11:665929. 10.3389/fonc.2021.665929. PubMed PMC

Naito Y, Tsuneki M, Fukushima N, Koga Y, Higashi M, Notohara K, Aishima S, Ohike N, Tajiri T, Yamaguchi H, Fukumura Y, Kojima M, Hirabayashi K, Hamada Y, Norose T, Kai K, Omori Y, Sukeda A, Noguchi H, Uchino K, Itakura J, Okabe Y, Yamada Y, Akiba J, Kanavati F, Oda Y, Furukawa T, Yano H. A deep learning model to detect pancreatic ductal adenocarcinoma on endoscopic Ultrasound-Guided Fine-Needle biopsy. Sci Rep. 2021;11(1):8454. 10.1038/s41598-021-87748-0. PubMed PMC

Kriegsmann M, Kriegsmann K, Steinbuss G, Zgorzelski C, Kraft A, Gaida MM. Deep learning in pancreatic tissue: identification of anatomical structures, pancreatic intraepithelial neoplasia, and ductal adenocarcinoma. Int J Mol Sci. 2021;22(10):5385. 10.3390/ijms22105385. PubMed PMC

Janssen BV, Oteman B, Ali M, Valkema PA, Adsay V, Basturk O, Chatterjee D, Chou A, Crobach S, Doukas M, Drillenburg P, Esposito I, Gill AJ, Hong S-M, Jansen C, Kliffen M, Mittal A, Samra J, van Velthuysen M-LF, Yavas A, Kazemier G, Verheij J, Steyerberg E, Besselink MG, Wang H, Verbeke C, Fariña A, de Boer OJ, Pathologists. (ISGPP), for the I. S. G. of P.; consortium, the P. and H. A. I. R. (PHAIR); pathologists (ISGPP), for the I. S. G. of P.; consortium, the P. and H. A. I. R. (PHAIR). Artificial Intelligence-Based segmentation of residual pancreatic Cancer in resection specimens following neoadjuvant treatment (ISGPP-2): international improvement and validation study. Am J Surg Pathol. 2024;48(9):1108. 10.1097/PAS.0000000000002270. PubMed PMC

Sehmi MNM, Fauzi MFA, Ahmad WSHMW, Chan EWL. Pancreatic Cancer grading in pathological images using deep learning convolutional neural networks. F1000Research October 18, 2021. 10.12688/f1000research.73161.1 PubMed PMC

Ghoshal B, Ghoshal B, Tucker A. Leveraging uncertainty in deep learning for pancreatic adenocarcinoma grading. In: Yang G, Aviles-Rivero A, Roberts M, Schönlieb C-B, editors. Medical image Understanding and analysis. Springer International Publishing: Cham,; 2022. pp. 565–77. 10.1007/978-3-031-12053-4_42.

Ahmadvand P, Farahani H, Farnell D, Darbandsari A, Topham J, Karasinska J, Nelson J, Naso J, Jones SJM, Renouf D, Schaeffer DF, Bashashati A. A deep learning approach for the identification of the molecular subtypes of pancreatic ductal adenocarcinoma based on whole slide pathology images. Am J Pathol. 2024;194(12):2302–12. 10.1016/j.ajpath.2024.08.006. PubMed

Wilmink JW, Besselink MG, Brosens LAA, Wang H, Verbeke CS, Verheij J. Amsterdam international consensus meeting: tumor response scoring in the pathology assessment of resected pancreatic Cancer after neoadjuvant therapy. Mod Pathol. 2021;34(1):4–12. 10.1038/s41379-020-00683-9. VelthuysenM.-L. F.Basturk, O.; Campbell, F.; Doglioni, C.; Esposito, I.; Feakins, R.; Fukushima, N.; Gill, A. J.; Hruban, R. H.; Kaplan, J.; Koerkamp, B. G.; Hong, S.-M.; Krasinskas, A.; Luchini, C.; Offerhaus, J.; Sarasqueta, A. F.; Shi, C.; Singhi, A.; Stoop, T. F.; Soer, E. C.; Thompson, E.; Tienhoven, G. van. PubMed

Fahrmann JF, Schmidt CM, Mao X, Irajizad E, Loftus M, Zhang J, Patel N, Vykoukal J, Dennison JB, Long JP, Do K-A, Zhang J, Chabot JA, Kluger MD, Kastrinos F, Brais L, Babic A, Jajoo K, Lee LS, Clancy TE, Ng K, Bullock A, Genkinger J, Yip-Schneider MT, Maitra A, Wolpin BM, Hanash S. Lead-Time trajectory of CA19-9 as an anchor marker for pancreatic Cancer early detection. Gastroenterology. 2021;160(4):1373–e13836. 10.1053/j.gastro.2020.11.052. PubMed PMC

Poruk KE, Gay DZ, Brown K, Mulvihill JD, Boucher KM, Scaife CL, Firpo MA, Mulvihill SJ. The clinical utility of CA 19– 9 in pancreatic adenocarcinoma: diagnostic and prognostic updates. Curr Mol Med. 2013;13(3):340–51. 10.2174/1566524011313030003. PubMed PMC

Majumder S, Taylor WR, Foote PH, Berger CK, Wu CW, Mahoney DW, Bamlet WR, Burger KN, Postier N, de la Fuente J, Doering KA, Lidgard GP, Allawi HT, Petersen GM, Chari ST, Ahlquist DA, Kisiel JB. High detection rates of pancreatic Cancer across stages by plasma assay of novel methylated DNA markers and CA19-9. Clin Cancer Res Off J Am Assoc Cancer Res. 2021;27(9):2523–32. 10.1158/1078-0432.CCR-20-0235. PubMed PMC

Mahawan T, Luckett T, Mielgo Iza A, Pornputtapong N, Caamaño Gutiérrez E. Robust and consistent biomarker candidates identification by a machine learning approach applied to pancreatic ductal adenocarcinoma metastasis. BMC Med Inf Decis Mak. 2024;24(Suppl 4):175. 10.1186/s12911-024-02578-0. PubMed PMC

Iwano T, Yoshimura K, Watanabe G, Saito R, Kiritani S, Kawaida H, Moriguchi T, Murata T, Ogata K, Ichikawa D, Arita J, Hasegawa K, Takeda S. High-Performance collective biomarker from liquid biopsy for diagnosis of pancreatic Cancer based on mass spectrometry and machine learning. J Cancer. 2021;12(24):7477–87. 10.7150/jca.63244. PubMed PMC

Karar ME, El-Fishawy N, Radad M. Automated classification of urine biomarkers to diagnose pancreatic Cancer using 1-D convolutional neural networks. J Biol Eng. 2023;17:28. 10.1186/s13036-023-00340-0. PubMed PMC

Athanasiou A, Kureshi N, Wittig A, Sterner M, Huber R, Palma NA, King T, Schiess R. Biomarker discovery for early detection of pancreatic ductal adenocarcinoma (PDAC) using multiplex proteomics technology. J Proteome Res. 2024;24(1):315–22. 10.1021/acs.jproteome.4c00752. PubMed PMC

Firpo MA, Boucher KM, Bleicher J, Khanderao GD, Rosati A, Poruk KE, Kamal S, Marzullo L, De Marco M, Falco A, Genovese A, Adler JM, De Laurenzi V, Adler DG, Affolter KE, Garrido-Laguna I, Scaife CL, Turco MC, Mulvihill SJ. Multianalyte serum biomarker panel for early detection of pancreatic adenocarcinoma. JCO Clin Cancer Inf. 2023;7:e2200160. 10.1200/CCI.22.00160. PubMed PMC

Lee J, Kang SW, Sim E-J, Bae J-S, Koo S, Byoun M, Kwon S, Hong S, Kim Y, Youn Y, Jung K, Kim J, Jeong HH, Kim J, Hwang J-H. Novel mRNA Biomarker-Based liquid biopsy for the detection of resectable pancreatic Cancer. BMC Cancer. 2025;25:762. 10.1186/s12885-025-14124-w. PubMed PMC

Yu S, Li Y, Liao Z, Wang Z, Wang Z, Li Y, Qian L, Zhao J, Zong H, Kang B, Zou W-B, Chen K, He X, Meng Z, Chen Z, Huang S, Wang P. Plasma extracellular vesicle long RNA profiling identifies a diagnostic signature for the detection of pancreatic ductal adenocarcinoma. 2020. 10.1136/gutjnl-2019-318860 PubMed

Chen Y, Nian F, Chen J, Jiang Q, Yuan T, Feng H, Shen X, Dong L. Metagenomic microbial signatures for noninvasive detection of pancreatic Cancer. Biomedicines. 2025;13(4):1000. 10.3390/biomedicines13041000. PubMed PMC

Wolrab D, Jirásko R, Cífková E, Höring M, Mei D, Chocholoušková M, Peterka O, Idkowiak J, Hrnčiarová T, Kuchař L, Ahrends R, Brumarová R, Friedecký D, Vivo-Truyols G, Škrha P, Škrha J, Kučera R, Melichar B, Liebisch G, Burkhardt R, Wenk MR, Cazenave-Gassiot A, Karásek P, Novotný I, Greplová K, Hrstka R, Holčapek M. Lipidomic profiling of human serum enables detection of pancreatic Cancer. Nat Commun. 2022;13(1):124. 10.1038/s41467-021-27765-9. PubMed PMC

Chen RJ, Lu MY, Williamson DFK, Chen TY, Lipkova J, Noor Z, Shaban M, Shady M, Williams M, Joo B, Mahmood F. Pan-Cancer integrative Histology-Genomic analysis via multimodal deep learning. Cancer Cell. 2022;40(8):865–e8786. 10.1016/j.ccell.2022.07.004. PubMed PMC

Hou J, Jia X, Xie Y, Qin W. Integrative Histology-Genomic analysis predicts hepatocellular carcinoma prognosis using deep learning. Genes. 2022;13(10):1770. 10.3390/genes13101770. PubMed PMC

Mobadersany P, Yousefi S, Amgad M, Gutman DA, Barnholtz-Sloan JS, Velázquez Vega JE, Brat DJ, Cooper L. A. D. Predicting Cancer Outcomes from Histology and Genomics Using Convolutional Networks. PubMed PMC

Höhn J, Krieghoff-Henning E, Jutzi TB, Kalle C. Combining CNN-Based histologic whole slide image analysis and patient data to improve skin Cancer classification. Eur J Cancer. 2021;149:94–101. 10.1016/j.ejca.2021.02.032. UtikalJ. S.Meier, F.; Gellrich, F. F.; Hobelsberger, S.; Hauschild, A.; Schlager, J. G.; French, L.; Heinzerling, L.; Schlaak, M.; Ghoreschi, K.; Hilke, F. J.; Poch, G.; Kutzner, H.; Heppt, M. V.; Haferkamp, S.; Sondermann, W.; Schadendorf, D.; Schilling, B.; Goebeler, M.; Hekler, A.; Fröhling, S.; Lipka, D. B.; Kather, J. N.; Krahl, D.; Ferrara, G.; Haggenmüller, S.; Brinker, T. J. PubMed

Jabbar HK, Khan RZ. Methods to avoid Over-Fitting and Under-Fitting in supervised machine learning (Comparative Study). Computer science, communication and instrumentation devices. Research Publishing Services; 2014. pp. 163–72. 10.3850/978-981-09-5247-1_017.

Ying X. An overview of overfitting and its solutions. J Phys Conf Ser. 2019;1168:022022. 10.1088/1742-6596/1168/2/022022.

Komura D, Ishikawa S. Machine learning methods for histopathological image analysis. Comput Struct Biotechnol J. 2018;16:34–42. 10.1016/j.csbj.2018.01.001. PubMed PMC

Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Advances in neural information processing systems. Volume 25. Curran Associates, Inc.; 2012.

Srinidhi CL, Ciga O, Martel AL. Deep neural network models for computational histopathology: A survey. Med Image Anal. 2021;67:101813. 10.1016/j.media.2020.101813. PubMed PMC

He K, Girshick R, Dollár PR. ImageNet Pre-Training. arXiv November 21, 2018. 10.48550/arXiv.1811.08883

Litjens G, Bandi P, Ehteshami Bejnordi B, Geessink O, Balkenhol M, Bult P, Halilovic A, Hermsen M, van de Loo R, Vogels R, Manson QF, Stathonikos N, Baidoshvili A, van Diest P, Wauters C, van Dijk M, van der Laak J. 1399 H&E-Stained Sentinel lymph node sections of breast Cancer patients: the CAMELYON dataset. GigaScience. 2018;7(6):giy065. 10.1093/gigascience/giy065. PubMed PMC

Aresta G, Araújo T, Kwok S, Chennamsetty SS, Safwan M, Alex V, Marami B, Prastawa M, Chan M, Donovan M, Fernandez G, Zeineh J, Kohl M, Walz C, Ludwig F, Braunewell S, Baust M, Vu QD, To MNN, Kim E, Kwak JT, Galal S, Sanchez-Freire V, Brancati N, Frucci M, Riccio D, Wang Y, Sun L, Ma K, Fang J, Kone I, Boulmane L, Campilho A, Eloy C, Polónia A, Aguiar PBACH. Grand challenge on breast Cancer histology images. Med Image Anal. 2019;56:122–39. 10.1016/j.media.2019.05.010. PubMed

Bulten W, Kartasalo K, Chen P-HC, Ström P, Pinckaers H, Nagpal K, Cai Y, Steiner DF, van Boven H, Vink R, Hulsbergen-van de Kaa C, van der Laak J, Amin MB, Evans AJ, van der Kwast T, Allan R, Humphrey PA, Grönberg H, Samaratunga H, Delahunt B, Tsuzuki T, Häkkinen T, Egevad L, Demkin M, Dane S, Tan F, Valkonen M, Corrado GS, Peng L, Mermel CH, Ruusuvuori P, Litjens G, Eklund M. PANDA challenge consortium. Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge. Nat Med. 2022;28(1):154–63. 10.1038/s41591-021-01620-2. PubMed PMC

Babaie M, Kalra S, Sriram A, Mitcheltree C, Zhu S, Khatami A, Rahnamayan S, Tizhoosh HR. Classification and retrieval of digital pathology scans. A New Dataset; 2017. pp. 8–16.

Schuurmans M, Alves N, Vendittelli P, Huisman H, Hermans J, Litjens G, Chang D, Verbeke C, Malats N, Löhr M. Artificial intelligence in pancreatic ductal adenocarcinoma imaging: A commentary on potential future applications. Gastroenterology. 2023;165(2):309–16. 10.1053/j.gastro.2023.04.003. PubMed

Collisson EA, Sadanandam A, Olson P, Gibb WJ, Truitt M, Gu S, Cooc J, Weinkle J, Kim GE, Jakkula L, Feiler HS, Ko AH, Olshen AB, Danenberg KL, Tempero MA, Spellman PT, Hanahan D, Gray JW. Subtypes of pancreatic ductal adenocarcinoma and their differing responses to therapy. Nat Med. 2011;17(4):500–3. 10.1038/nm.2344. PubMed PMC

Carrillo-Perez F, Ortuno FM, Börjesson A, Rojas I, Herrera LJ. Performance comparison between Multi-Center histopathology datasets of a Weakly-Supervised deep learning model for pancreatic ductal adenocarcinoma detection. Cancer Imaging. 2023;23:66. 10.1186/s40644-023-00586-3. PubMed PMC

Pečinka L, Moráň L, Kovačovicová P, Meloni F, Havel J, Pivetta T, Vaňhara P. Intact cell mass spectrometry coupled with machine learning reveals minute changes induced by single gene Silencing. Heliyon. 2024;10(9):e29936. 10.1016/j.heliyon.2024.e29936. PubMed PMC

Li X, Plataniotis KN. A complete color normalization approach to histopathology images using color cues computed from Saturation-Weighted statistics. IEEE Trans Biomed Eng. 2015;62(7):1862–73. 10.1109/TBME.2015.2405791. PubMed

Monaco J, Hipp J, Lucas D, Smith S, Balis U, Madabhushi A. Image segmentation with implicit color standardization using spatially constrained expectation maximization: detection of nuclei. In: Ayache N, Delingette H, Golland P, Mori K, editors. PubMed

Linmans J, Laak J. van der; Litjens, G. Efficient Out-of-Distribution Detection in Digital Pathology Using Multi-Head Convolutional Neural Networks. In

Pawlowski N, Bhooshan S, Ballas N, Ciompi F, Glocker B, Drozdzal M. Needles in haystacks: on classifying tiny objects in large images. ArXiv January. 2020;6. 10.48550/arXiv.1908.06037.

Baxi V, Edwards R, Montalto M, Saha S. Digital pathology and artificial intelligence in translational medicine and clinical practice. Mod Pathol. 2022;35(1):23–32. 10.1038/s41379-021-00919-2. PubMed PMC

Felländer-Tsai LAI, Ethics. Accountability, and sustainability: revisiting the hippocratic oath. Acta Orthop. 2019;91(1):1–2. 10.1080/17453674.2019.1682850. PubMed PMC

Shreve JT, Khanani SA, Haddad TC. Artificial intelligence in oncology: current capabilities, future opportunities, and ethical considerations. Am Soc Clin Oncol Educ Book 2022, 42, 842–51. 10.1200/EDBK_350652 PubMed

Kiener M. Artificial intelligence in medicine and the disclosure of risks. AI Soc. 2021;36(3):705–13. 10.1007/s00146-020-01085-w. PubMed PMC

Loftus TJ, Tighe PJ, Filiberto AC, Efron PA, Brakenridge SC, Mohr AM, Rashidi P, Upchurch GR, Bihorac J. Artificial intelligence and surgical Decision-Making. JAMA Surg. 2020;155(2):148–58. 10.1001/jamasurg.2019.4917. PubMed PMC

Abdullah YI, Schuman JS, Shabsigh R, Caplan A, Al-Aswad LA. Ethics of artificial intelligence in medicine and ophthalmology. Asia-Pac J Ophthalmol. 2021;10(3):289–98. 10.1097/APO.0000000000000397. PubMed PMC