Independent real‐world application of a clinical‐grade automated prostate cancer detection system
Open accessAbstract
Artificial intelligence (AI)‐based systems applied to histopathology whole‐slide images have the potential to improve patient care through mitigation of challenges posed by diagnostic variability, histopathology caseload, and shortage of pathologists. We sought to define the performance of an AI‐based automated prostate cancer detection system, Paige Prostate, when applied to independent real‐world data. The algorithm was employed to classify slides into two categories: benign (no further review needed) or suspicious (additional histologic and/or immunohistochemical analysis required). We assessed the sensitivity, specificity, positive predictive values (PPVs), and negative predictive values (NPVs) of a local pathologist, two central pathologists, and Paige Prostate in the diagnosis of 600 transrectal ultrasound‐guided prostate needle core biopsy regions (‘part‐specimens’) from 100 consecutive patients, and to ascertain the impact of Paige Prostate on diagnostic accuracy and efficiency. Paige Prostate displayed high sensitivity (0.99; CI 0.96–1.0), NPV (1.0; CI 0.98–1.0), and specificity (0.93; CI 0.90–0.96) at the part‐specimen level. At the patient level, Paige Prostate displayed optimal sensitivity (1.0; CI 0.93–1.0) and NPV (1.0; CI 0.91–1.0) at a specificity of 0.78 (CI 0.64–0.89). The 27 part‐specimens considered by Paige Prostate as suspicious, whose final diagnosis was benign, were found to comprise atrophy (n = 14), atrophy and apical prostate tissue (n = 1), apical/benign prostate tissue (n = 9), adenosis (n = 2), and post‐atrophic hyperplasia (n = 1). Paige Prostate resulted in the identification of four additional patients whose diagnoses were upgraded from benign/suspicious to malignant. Additionally, this AI‐based test provided an estimated 65.5% reduction of the diagnostic time for the material analyzed. Given its optimal sensitivity and NPV, Paige Prostate has the potential to be employed for the automated identification of patients whose histologic slides could forgo full histopathologic review. In addition to providing incremental improvements in diagnostic accuracy and efficiency, this AI‐based system identified patients whose prostate cancers were not initially diagnosed by three experienced histopathologists. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.
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Using multiple-instance learning with labels at the level of groups of slides has enabled clinically relevant diagnostics by scaling to training datasets on the order of 10,000 WSIs.
“Using multiple-instance learning14,49,55 with labels at the level of groups of slides has enabled clinically relevant diagnostics by scaling to training datasets on the order of 10,000 WSIs10–14.”
From A foundation model for clinical-grade computational pathology and rare cancers detection (2024) · cites this paper as [11]
Paige Prostate Alpha was validated in an external cohort and cleared by the FDA.
“The platform was subsequently validated in an external cohort (61) and eventually cleared by the FDA (DEN200080, RRID:SCR_012945).”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [61]
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Cites
SEER Cancer Statistics Review, 1975-2003
Ries, L. A. G., Harkins, D., Krapcho, M. F., Mariotto, A. B., Miller, B., Feuer, E. J., Clegg, L. X., Eisner, M. P., Horner, M., Howlader, N., et al. (2006) · Digital Archive @ GSU · Cited by 11,404 · see citing works
Clinical-grade computational pathology using weakly supervised deep learning on whole slide images
Campanella, G., Hanna, M. G., Geneslaw, L., Miraflor, A., Werneck Krauss Silva, V., Busam, K. J., Brogi, E., Reuter, V. E., Klimstra, D. S., & Fuchs, T. J. (2019) · Nature Medicine · doi:10.1038/s41591-019-0508-1 · Cited by 2,799 · see citing works
Self-citationBulten, W., Pinckaers, H., van Boven, H., Vink, R., de Bel, T., van Ginneken, B., van der Laak, J., Hulsbergen-van de Kaa, C., & Litjens, G. (2020) · The Lancet Oncology · doi:10.1016/s1470-2045(19)30739-9 · Cited by 705 · see citing works
Ström, P., Kartasalo, K., Olsson, H., Solorzano, L., Delahunt, B., Berney, D. M., Bostwick, D. G., Evans, A. J., Grignon, D. J., Humphrey, P. A., et al. (2020) · The Lancet Oncology · doi:10.1016/s1470-2045(19)30738-7 · Cited by 620 · see citing works
Rashidi, H. H., Tran, N. K., Betts, E. V., Howell, L. P., & Green, R. (2019) · Academic Pathology · doi:10.1177/2374289519873088 · Cited by 381 · see citing works
Pantanowitz, L., Quiroga‐Garza, G. M., Bien, L., Heled, R., Laifenfeld, D., Linhart, C., Sandbank, J., Shach, A. A., Shalev, V., Vecsler, M., et al. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30159-x · Cited by 361 · see citing works
Trends in the US and Canadian Pathologist Workforces From 2007 to 2017
Metter, D. M., Colgan, T. J., Leung, S. T., Timmons, C. F., & Park, J. Y. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.4337 · Cited by 293 · see citing works
Pantanowitz, L., Sharma, A., Carter, A. B., Kurc, T., Sussman, A., & Saltz, J. (2018) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_69_18 · Cited by 282 · see citing works
Guidance for laboratories performing molecular pathology for cancer patients
Cree, I. A., Deans, Z. C., Ligtenberg, M. J. L., Normanno, N., Edsjö, A., Rouleau, É., Solé, F. G., Thunnissen, E., Timens, W., Schuuring, E. M. D., et al. (2014) · Journal of Clinical Pathology · doi:10.1136/jclinpath-2014-202404 · Cited by 212 · see citing works
Raciti, P., Sue, J., Ceballos, R., Godrich, R., Kunz, J. D., Kapur, S., Reuter, V., Grady, L., Kanan, C., Klimstra, D. S., et al. (2020) · Modern Pathology · doi:10.1038/s41379-020-0551-y · Cited by 186 · cited 2× · see citing works
“AI-based approaches to address these unmet needs in the context of diagnostic prostate cancer diagnosis have showed initial promise [7 – 10 , 12 – 15 ].”
BackgroundMethodSelf-citationBulten, W., Balkenhol, M. C. A., Belinga, J.-J. A., Brilhante, A., Çakır, A., Egevad, L., Eklund, M., Farré, X., Geronatsiou, K., Molinié, V., et al. (2020) · Modern Pathology · doi:10.1038/s41379-020-0640-y · Cited by 183 · see citing works
Self-citationGuidelines for processing and reporting of prostatic needle biopsies
van der Kwast, T., Lopes, C., Santonja, C., Pihl, C.-G., Neetens, I., Martikainen, P., Lollo, S. D., Bubendorf, L., & Hoedemaeker, R. F. (2003) · Journal of Clinical Pathology · doi:10.1136/jcp.56.5.336 · Cited by 120 · see citing works
Bulten, W., Pinckaers, H., van Boven, H., Vink, R., de Bel, T., van Ginneken, B., van der Laak, J. A. W. M., van de Kaa, C. A. H. ‐., & Litjens, G. J. S. (2020) · arXiv (Cornell University) · Cited by 106 · see citing works
False-negative Prostate Needle Biopsies: Frequency, Histopathologic Features, and Follow-up
Wolters, T., van der Kwast, T., Vissers, C. J., Bangma, C. H., Roobol, M. J., Schröder, F. H., & van Leenders, G. J. L. H. (2009) · The American Journal of Surgical Pathology · doi:10.1097/pas.0b013e3181c3ece9 · Cited by 88 · see citing works
The Future of Telepathology for the Developing World
Hitchcock, C. L. (2011) · Archives of Pathology & Laboratory Medicine · doi:10.5858/135.2.211 · Cited by 66 · see citing works
Têtu, B., & Evans, A. J. (2013) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2013-0289-ed · Cited by 54 · see citing works
Regulating Artificial Intelligence for a Successful Pathology Future
Allen, T. C. (2019) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2019-0229-ed · Cited by 43 · see citing works
Lesions missed on prostate biopsies in cases sent in for consultation
Kronz, J. D., Milord, R. A., Wilentz, R. E., Weir, E. G., Schreiner, S. R., & Epstein, J. I. (2003) · The Prostate · doi:10.1002/pros.10182 · Cited by 29 · see citing works
False-Negative Histopathologic Diagnosis of Prostatic Adenocarcinoma
Yang, C., & Humphrey, P. A. (2019) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2019-0456-ra · Cited by 22 · see citing works
Clinical deployment of AI for prostate cancer diagnosis
Janowczyk, A. R., Leo, P., & Rubin, M. A. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30163-1 · Cited by 11 · see citing works
Flagged first, then by citations
1 of the 21 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Deep learning in cancer diagnosis, prognosis and treatment selection
Tran, K. A., Kondrashova, O., Bradley, A., Williams, E. D., Pearson, J. V., & Waddell, N. (2021) · Genome Medicine · doi:10.1186/s13073-021-00968-x · Cited by 932 · see citing works
A foundation model for clinical-grade computational pathology and rare cancers detection
Vorontsov, E., Bozkurt, A., Casson, A., Shaikovski, G., Zelechowski, M., Severson, K., Zimmermann, E., Hall, J., Tenenholtz, N., Fusi, N., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-03141-0 · Cited by 572 · see citing works
Self-citationThe Role of Artificial Intelligence in Early Cancer Diagnosis
Hunter, B., Hindocha, S., & Lee, R. W. (2022) · Cancers · doi:10.3390/cancers14061524 · Cited by 367 · see citing works
Artificial intelligence in diagnostic pathology
Shafi, S., & Parwani, A. V. (2023) · Diagnostic Pathology · doi:10.1186/s13000-023-01375-z · Cited by 350 · see citing works
McGenity, C., Clarke, E. L., Jennings, C., Matthews, G., Cartlidge, C., Freduah-Agyemang, H., Stocken, D. D., & Treanor, D. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01106-8 · Cited by 290 · see citing works
Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions
Lotter, W., Hassett, M. J., Schultz, N., Kehl, K. L., Van Allen, E. M., & Cerami, E. (2024) · Cancer Discovery · doi:10.1158/2159-8290.cd-23-1199 · Cited by 196 · see citing works
Integrating digital pathology into clinical practice
Hanna, M. G., Ardon, O., Reuter, V. E., Sirintrapun, S. J., England, C., Klimstra, D. S., & Hameed, M. (2021) · Modern Pathology · doi:10.1038/s41379-021-00929-0 · Cited by 157 · see citing works
Self-citationGenerative-AI-Driven Human Digital Twin in IoT Healthcare: A Comprehensive Survey
Chen, J., Shi, Y., Yi, C., Du, H., Kang, J., & Niyato, D. T. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3421918 · Cited by 155 · see citing works
Eloy, C., Marques, A., Pinto, J. C., Pinheiro, J., Campelos, S., Curado, M., Vale, J., & Polónia, A. (2023) · Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin · doi:10.1007/s00428-023-03518-5 · Cited by 118 · see citing works
Raciti, P., Sue, J., Retamero, J. A., Ceballos, R., Godrich, R., Kunz, J. D., Casson, A., Thiagarajan, D., Ebrahimzadeh, Z., Viret, J., et al. (2023) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2022-0066-oa · Cited by 119 · see citing works
Self-citationSandbank, J., Bataillon, G., Nudelman, A., Krasnitsky, I., Mikulinsky, R., Bien, L., Thibault, L., Albrecht Shach, A., Sebag, G., Clark, D. P., et al. (2022) · npj Breast Cancer · doi:10.1038/s41523-022-00496-w · Cited by 112 · see citing works
Explainability and causability in digital pathology
Plass, M., Kargl, M., Kiehl, T., Regitnig, P., Geißler, C., Evans, T., Zerbe, N., Carvalho, R., Holzinger, A., & Müller, H. (2023) · The Journal of Pathology Clinical Research · doi:10.1002/cjp2.322 · Cited by 99 · see citing works
Implementation of Digital Pathology and Artificial Intelligence in Routine Pathology Practice
Zhang, D. Y., Venkat, A., Khasawneh, H., Sali, R., Zhang, V., & Pei, Z. (2024) · Laboratory Investigation · doi:10.1016/j.labinv.2024.102111 · Cited by 91 · see citing works
Bakrania, A. K., Joshi, N., Zhao, X., Zheng, G., & Bhat, M. (2023) · Pharmacological Research · doi:10.1016/j.phrs.2023.106706 · Cited by 96 · see citing works
Homeyer, A., Geißler, C., Schwen, L. O., Zakrzewski, F., Evans, T., Strohmenger, K., Westphal, M., Bülow, R. D., Kargl, M., Karjauv, A., et al. (2022) · Modern Pathology · doi:10.1038/s41379-022-01147-y · Cited by 83 · see citing works
Prabhudesai, S., Yang, L., Asthana, S., Huan, X., Liao, Q. V., & Banovic, N. (2023) · Proceedings of the 28th International Conference on Intelligent User Interfaces · doi:10.1145/3581641.3584033 · Cited by 82 · see citing works
Machine learning methods for histopathological image analysis: updates in 2024
Komura, D., Ochi, M., & Ishikawa, S. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.12.033 · Cited by 76 · see citing works
Artificial intelligence in digital pathology — time for a reality check
Aggarwal, A., Bharadwaj, S., Corredor, G., Pathak, T. B., Badve, S., & Madabhushi, A. (2025) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-025-00991-6 · Cited by 78 · see citing works
Artificial Intelligence Advancements in Oncology: A Review of Current Trends and Future Directions
Huhulea, E. N., Huang, L., Eng, S., Sumawi, B., Huang, A., Aifuwa, E., Hirani, R., Tiwari, R. K., & Etienne, M. (2025) · Biomedicines · doi:10.3390/biomedicines13040951 · Cited by 74 · see citing works
Benchmarking foundation models as feature extractors for weakly supervised computational pathology
Neidlinger, P., El Nahhas, O. S. M., Muti, H. S., Lenz, T., Hoffmeister, M., Brenner, H., van Treeck, M., Langer, R., Dislich, B., Behrens, H. M., et al. (2025) · Nature Biomedical Engineering · doi:10.1038/s41551-025-01516-3 · Cited by 80 · see citing works
Artificial intelligence-based biomarkers for treatment decisions in oncology
Ligero, M., Nahhas, O. S. M. E., Aldea, M. D., & Kather, J. N. (2025) · Trends in cancer · doi:10.1016/j.trecan.2024.12.001 · Cited by 70 · see citing works
Reducing the workload of medical diagnosis through artificial intelligence: A narrative review
Jeong, J., Kim, S., Pan, L., Hwang, D.-Y., Kim, D., Choi, J., Kwon, Y., Yi, P., Jeong, J., & Yoo, S. (2025) · Medicine · doi:10.1097/md.0000000000041470 · Cited by 61 · see citing works
Advancements in pathology: Digital transformation, precision medicine, and beyond
Ahuja, S., & Zaheer, S. (2024) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2024.100408 · Cited by 64 · see citing works
Tolkach, Y., Wolgast, L. M., Damanakis, A. I., Pryalukhin, A. E., Schallenberg, S., Hulla, W., Eich, M., Schroeder, W., Mukhopadhyay, A., Fuchs, M., et al. (2023) · The Lancet Digital Health · doi:10.1016/s2589-7500(23)00027-4 · Cited by 55 · see citing works
Harnessing artificial intelligence for prostate cancer management
Zhu, L., Pan, J., Mou, W., Deng, L., Zhu, Y., Wang, Y., Pareek, G., Hyams, E. S., Carneiro, B. A., Hadfield, M. J., et al. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2024.101506 · Cited by 56 · see citing works
Kim, D., Sundling, K. E., Virk, R. K., Thrall, M. J., Alperstein, S. A., Bui, M. M., Chen, H., Donnelly, A. D., Lin, O., Liu, X., et al. (2023) · Journal of the American Society of Cytopathology · doi:10.1016/j.jasc.2023.11.006 · Cited by 53 · see citing works
Artificial intelligence–based algorithms for the diagnosis of prostate cancer: A systematic review
Marletta, S., Eccher, A., Martelli, F. M., Santonicco, N., Girolami, I., Scarpa, A., Pagni, F., L’Imperio, V., Pantanowitz, L., Gobbo, S., et al. (2024) · American Journal of Clinical Pathology · doi:10.1093/ajcp/aqad182 · Cited by 55 · see citing works
Shirzad, M., Salahvarzi, A., Razzaq, S., Javid‐Naderi, M. J., Rahdar, A., Fathi‐karkan, S., Ghadami, A., Kharaba, Z. J., & Ferreira, L. F. R. (2025) · Critical Reviews in Oncology/Hematology · doi:10.1016/j.critrevonc.2025.104653 · Cited by 58 · see citing works
Hanna, M. G., Olson, N. H., Zarella, M. D., Dash, R. C., Herrmann, M. D., Furtado, L. V., Stram, M. N., Raciti, P. M., Hassell, L. A., Mays, A., et al. (2023) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2023-0042-cp · Cited by 51 · see citing works
Self-citationArtificial intelligence in cancer: applications, challenges, and future perspectives
Cheng, H., & Shi, S.-S. (2025) · Molecular Cancer · doi:10.1186/s12943-025-02450-3 · Cited by 57 · see citing works
Riaz, I. B., Harmon, S. A., Chen, Z., Naqvi, S. A. A., & Liang Cheng, C. (2024) · American Society of Clinical Oncology Educational Book · doi:10.1200/edbk_438516 · Cited by 49 · see citing works
Morozov, A., Taratkin, M. S., Bazarkin, A., Rivas, J. G., Puliatti, S., Checcucci, E., BELENCHON, I. R., Kowalewski, K., Shpikina, A., Singla, N., et al. (2023) · Prostate Cancer and Prostatic Diseases · doi:10.1038/s41391-023-00673-3 · Cited by 46 · see citing works
Multi-modality artificial intelligence in digital pathology
Qiao, Y., Zhao, L., Luo, C., Luo, Y., Wu, Y., Li, S., Bu, D., & Zhao, Y. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac367 · Cited by 44 · see citing works
Deep Learning Methodologies Applied to Digital Pathology in Prostate Cancer: A Systematic Review
Rabilloud, N., Allaume, P., Acosta, O., de Crevoisier, R., Bourgade, R., Loussouarn, D., Rioux-Leclercq, N. C., Khene, Z., Mathiéu, R., Bensalah, K., et al. (2023) · Diagnostics · doi:10.3390/diagnostics13162676 · Cited by 47 · see citing works
Geaney, A., O’Reilly, P., Maxwell, P., James, J. A., McArt, D., & Salto-Tellez, M. (2023) · Oncogene · doi:10.1038/s41388-023-02857-6 · Cited by 42 · see citing works
Artificial Intelligence–assisted Prostate Cancer Diagnosis: Radiologic-Pathologic Correlation
Mata, L. A., Retámero, J. A., Gupta, R. T., Figueras, R. G., & Luna, A. (2021) · Radiographics · doi:10.1148/rg.2021210020 · Cited by 42 · see citing works
Montezuma, D., Oliveira, S. P., Neto, P. C., Oliveira, D., Monteiro, A., Cardoso, J. S., & Macedo-Pinto, I. (2023) · Modern Pathology · doi:10.1016/j.modpat.2022.100086 · Cited by 40 · see citing works
Current status of machine learning in thyroid cytopathology
Wong, C. M., Kezlarian, B. E., & Lin, O. (2023) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2023.100309 · Cited by 35 · see citing works
Tolkach, Y., Ovtcharov, V., Pryalukhin, A. E., Eich, M., Gaisa, N. T., BRAUN, M. A., Radzhabov, A., Quaas, A., Hammerer, P., Dellmann, A., et al. (2023) · npj Precision Oncology · doi:10.1038/s41698-023-00424-6 · Cited by 35 · see citing works
Generating dermatopathology reports from gigapixel whole slide images with HistoGPT
Tran, M., Schmidle, P., Guo, R. R., Wagner, S. J., Koch, V., Lupperger, V., Novotny, B., Murphree, D. H., Hardway, H. D., D’Amato, M., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-60014-x · Cited by 36 · see citing works
“In this context, AI is used as a tool and complement to other medical tests, rather than as a replacement for pathologists 10 .”
Singh, A., Randive, S., Breggia, A., Ahmad, B., Christman, R., & Amal, S. (2023) · Cancers · doi:10.3390/cancers15235659 · Cited by 34 · see citing works
Novel Histopathological Biomarkers in Prostate Cancer: Implications and Perspectives
Kiełb, P., Kowalczyk, K., Gurwin, A., Nowak, Ł., Krajewski, W. R., Sosnowski, R., Szydełko, T., & Małkiewicz, B. (2023) · Biomedicines · doi:10.3390/biomedicines11061552 · Cited by 32 · see citing works
Evans, H., & Snead, D. R. J. (2023) · Histopathology · doi:10.1111/his.15071 · Cited by 33 · see citing works
Satturwar, S. P., & Parwani, A. V. (2024) · Advances in Anatomic Pathology · doi:10.1097/pap.0000000000000425 · Cited by 31 · see citing works
Arita, Y., Roest, C., Kwee, T. C., Paudyal, R., Lema-Dopico, A., Fransen, S. J., Hirahara, D., Takaya, E., Ueda, R., Ruby, L. C., et al. (2025) · Asian journal of urology · doi:10.1016/j.ajur.2024.12.001 · Cited by 31 · see citing works
Tsuneki, M., Abe, M., & Kanavati, F. (2022) · Diagnostics · doi:10.3390/diagnostics12030768 · Cited by 31 · see citing works
Mazo, C., Aura, C. M., Rahman, A., Gallagher, W. M., & Mooney, C. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12091496 · Cited by 31 · see citing works
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Faryna, K., Tessier, L., Retámero, J. A., Bonthu, S., Samanta, P., Singhal, N., Kammerer‐Jacquet, S., Radulescu, C., Agosti, V., Collin, A., et al. (2024) · Modern Pathology · doi:10.1016/j.modpat.2024.100563 · Cited by 28 · see citing works
Self-citationExploring the Use of Artificial Intelligence in the Management of Prostate Cancer
Chu, T. N., Wong, E. Y., Ma, R., Yang, C. H., Dalieh, I. S., & Hung, A. J. (2023) · Current Urology Reports · doi:10.1007/s11934-023-01149-6 · Cited by 28 · see citing works
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Khoraminia, F., Fuster, S., Kanwal, N., Olislagers, M., Engan, K., van Leenders, G. J. L. H., Stubbs, A., Akram, F., & Zuiverloon, T. C. M. (2023) · Cancers · doi:10.3390/cancers15184518 · Cited by 28 · see citing works
A comprehensive AI model development framework for consistent Gleason grading
Huo, X., Ong, K. H., Lau, K. W., Golé, L., Young, D. M., Tan, C. L., Zhu, X., Zhang, C., Zhang, Y., Li, L., et al. (2024) · Communications Medicine · doi:10.1038/s43856-024-00502-1 · Cited by 27 · see citing works
Artificial intelligence in pathologic diagnosis, prognosis and prediction of prostate cancer
Zhu, M. (2024) · American Journal of Clinical and Experimental Urology · doi:10.62347/jsae9732 · Cited by 27 · see citing works
Krishnamurthy, S., Schnitt, S. J., Vincent‐Salomon, A., Canas‐Marques, R., Colón, E., Kantekure, K., Maklakovski, M., Finck, W., Piana, J. T., Globerson, Y., et al. (2024) · JCO Precision Oncology · doi:10.1200/po.24.00353 · Cited by 27 · see citing works
A clinical road map for single-cell omics
Skinnider, M. A., Courtine, G., Bloch, J., & Squair, J. W. (2025) · Cell · doi:10.1016/j.cell.2025.06.009 · Cited by 28 · see citing works
Santa-Rosario, J. C., Gustafson, E., Bellassai, D. E. S., Gustafson, P. E., & de Socarraz, M. (2024) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2024.100378 · Cited by 26 · see citing works
Large Language Models in Oncology: Revolution or Cause for Concern?
Caglayan, A., Slusarczyk, W., Rabbani, R. D., Ghose, A., Papadopoulos, V. P., & Boussios, S. (2024) · Current Oncology · doi:10.3390/curroncol31040137 · Cited by 27 · see citing works
Applications of artificial intelligence in prostate cancer histopathology
Busby, D., Grauer, R., Pandav, K. B., Khosla, A., Jain, P., Menon, M., Haines, G. K., Cordon‐Cardo, C., Gorin, M. A., & Tewari, A. K. (2023) · Urologic Oncology Seminars and Original Investigations · doi:10.1016/j.urolonc.2022.12.002 · Cited by 24 · see citing works
Alqahtani, S. (2024) · Diagnostics · doi:10.3390/diagnostics14222576 · Cited by 23 · see citing works
Apornvirat, S., Namboonlue, C., & Laohawetwanit, T. (2024) · American Journal of Clinical Pathology · doi:10.1093/ajcp/aqae036 · Cited by 23 · see citing works
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Glass, C., Lafata, K. J., Jeck, W. R., Horstmeyer, R. W., Cooke, C., Everitt, J. I., Glass, M., Dov, D., & Seidman, M. A. (2021) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2021.11.008 · Cited by 18 · see citing works
Loaiza‐Bonilla, A., Thaker, N. G., Chung, C. D., Parikh, R. B., Stapleton, S., & Borkowski, P. (2025) · American Society of Clinical Oncology Educational Book · doi:10.1200/edbk-25-100048 · Cited by 16 · see citing works
Patel, A., Mohanty, S. K., & Parwani, A. V. (2022) · Surgical pathology clinics · doi:10.1016/j.path.2022.08.001 · Cited by 15 · see citing works
Yoder, A., Inge, L. J., Chen, C.-C., Marati, V. R., Nguyen, T., Zuiderveld, K. J., Martin, J. C., Gladden, S., Miri, M. S., Venugopal, R., et al. (2022) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2022.100116 · Cited by 14 · see citing works
Frewing, A., Gibson, A. B., Robertson, R., Urie, P. M., & Corte, D. D. (2023) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2022-0460-ra · Cited by 14 · see citing works
Current applications and challenges of artificial intelligence in pathology
Hanna, M. G., & Hanna, M. H. (2022) · Human Pathology Reports · doi:10.1016/j.hpr.2022.300596 · Cited by 14 · see citing works
Patel, A., Shaker, N., Mohanty, S. K., Sharma, S., Gangal, S., Eloy, C., & Parwani, A. V. (2022) · Diagnostics · doi:10.3390/diagnostics12081778 · Cited by 14 · see citing works
Artificial intelligence: a new era in prostate cancer diagnosis and treatment
Vidiyala, N., Parupathi, P. R., Sunkishala, P., Muppavarapu, C. S., Gujja, A., Kanagala, P., Meduri, S. K., & Nyavanandi, D. (2025) · International Journal of Pharmaceutics · doi:10.1016/j.ijpharm.2025.126024 · Cited by 13 · see citing works
Clinical Use of Molecular Biomarkers in Canine and Feline Oncology: Current and Future
Aupperle‐Lellbach, H., Kehl, A., de Brot, S., & van der Weyden, L. (2024) · Veterinary Sciences · doi:10.3390/vetsci11050199 · Cited by 13 · see citing works
The state of the art in artificial intelligence and digital pathology in prostate cancer
Ni, H. M., Kouzy, R., Sabbagh, A., Rooney, M. K., Feng, J., Castillo, S. P., Gadoue, S. M., Kouzi, Z. E., Hoffman, K. E., Yuan, Y., et al. (2025) · Nature Reviews Urology · doi:10.1038/s41585-025-01070-2 · Cited by 13 · see citing works
Artificial intelligence for detection of prostate cancer in biopsies during active surveillance
Arvidsson, I., Svanemur, E., Marginean, F. E., Simoulis, A., Overgaard, N. C., Åström, K., Heyden, A., Krzyzanowska, A., & Bjartell, A. S. (2024) · British Journal of Urology · doi:10.1111/bju.16456 · Cited by 12 · see citing works
Advancing Automatic Gastritis Diagnosis
Ma, M., Zeng, X., Qu, L., Sheng, X., Ren, H., Chen, W., Li, B., You, Q., Li, X., Wang, Y., et al. (2024) · American Journal Of Pathology · doi:10.1016/j.ajpath.2024.04.007 · Cited by 11 · see citing works
Chang, J., & Hatfield, B. S. (2024) · Advances in cancer research · doi:10.1016/bs.acr.2024.05.006 · Cited by 11 · see citing works
Paik, I., Lee, G., Lee, J.-H., Kwak, T.-Y., & Ha, H. K. (2025) · Prostate International · doi:10.1016/j.prnil.2025.02.002 · Cited by 11 · see citing works
Flach, R. N., van Dooijeweert, C., Nguyen, T. Q., Lynch, M., Jonges, T. G. N., Meijer, R. P., Suelmann, B. B. M., Willemse, P.-P. M., Stathonikos, N., & van Diest, P. J. (2025) · JCO Clinical Cancer Informatics · doi:10.1200/cci-24-00193 · Cited by 11 · see citing works
Gough, M., Liu, C., Srinivasan, B., Wilkinson, L., Dunk, L., Yang, Y., Schreiber, V., Tuffaha, H., Kryza, T., Hooper, J. D., et al. (2023) · Histopathology · doi:10.1111/his.15000 · Cited by 11 · see citing works
Irmakci, I., Nateghi, R., Zhou, R., Vescovo, M., Saft, M., Ross, A. E., Yang, X. J., Cooper, L., & Goldstein, J. A. (2024) · Modern Pathology · doi:10.1016/j.modpat.2024.100422 · Cited by 11 · see citing works
Automated histopathological evaluation of pterygium using artificial intelligence
Kim, J., Kim, Y. J., Lee, Y. J., Hyon, J. Y., Han, S. B., & Kim, K. G. (2022) · British Journal of Ophthalmology · doi:10.1136/bjophthalmol-2021-320141 · Cited by 10 · see citing works
The Role of Artificial Intelligence in Early Cancer Detection: Exploring Early Clinical Applications
Thaker, N. G., Dicker, A. P., Loaiza‐Bonilla, A., Wallace, A. F., Kolman, D., Ruggles, A. N. G., Manda, S., Frank, S. J., Orio, P. F., & Doria, C. (2024) · AI in Precision Oncology · doi:10.1089/aipo.2023.0011 · Cited by 10 · see citing works
Flach, R. N., Stathonikos, N., Nguyen, T. Q., ter Hoeve, N. D., van Diest, P. J., & van Dooijeweert, C. (2023) · BMJ Open · doi:10.1136/bmjopen-2022-067437 · Cited by 10 · see citing works
von Bahr, J., Diwan, V. K., Mårtensson, A., Linder, N., & Lundin, J. E. (2024) · JMIR Research Protocols · doi:10.2196/58149 · Cited by 9 · see citing works
Development of a deep learning system for predicting biochemical recurrence in prostate cancer
Cao, L., He, R., Zhang, A., Li, L., Cao, W., Liu, N., & Zhang, P. (2025) · BMC Cancer · doi:10.1186/s12885-025-13628-9 · Cited by 9 · see citing works
Computer-assisted diagnosis to improve diagnostic pathology: A review
Caputo, A., Maffei, E., Gupta, N., Cima, L., Merolla, F., Cazzaniga, G., Pepe, P., Verze, P., & Fraggetta, F. (2025) · Indian Journal of Pathology and Microbiology · doi:10.4103/ijpm.ijpm_339_24 · Cited by 9 · see citing works
An Overview of Artificial Intelligence in Gynaecological Pathology Diagnostics
Joshua, A., Allen, K. E., & Orsi, N. M. (2025) · Cancers · doi:10.3390/cancers17081343 · Cited by 9 · see citing works
Exploring multi-instance learning in whole slide imaging: Current and future perspectives
Yu, J., Chen, H., Hu, L., Wu, B., Zhou, S., Zhu, J., Jiang, Y., Han, S., & Wang, Z. (2025) · Pathology - Research and Practice · doi:10.1016/j.prp.2025.156006 · Cited by 8 · see citing works
Hassan, T., Hassan, B., El-Baz, A. S., & Werghi, N. (2021) · IEEE Sensors Applications Symposium (SAS) · doi:10.1109/sas51076.2021.9530155 · Cited by 9 · see citing works
Code-free machine learning for classification of central nervous system histopathology images
Jungo, P., & Hewer, E. (2023) · Journal of Neuropathology & Experimental Neurology · doi:10.1093/jnen/nlac131 · Cited by 9 · see citing works
Generating clinical-grade pathology reports from gigapixel whole slide images with HistoGPTPreprint
Tran, M., Schmidle, P., Wagner, S. J., Koch, V., Novotny, B. C., Lupperger, V., Feuchtinger, A., Böhner, A., Kaczmarczyk, R., Biedermann, T., et al. (2024) · medRxiv · doi:10.1101/2024.03.15.24304211 · Cited by 9 · see citing works
Stress testing deep learning models for prostate cancer detection on biopsies and surgical specimens
Flannery, B. T., Sandler, H. M., Lal, P., Feldman, M. D., Santa-Rosario, J. C., Pathak, T. B., Mirtti, T. K., Farré, X., Correa, R. J. M., Chafe, S., et al. (2024) · The Journal of Pathology · doi:10.1002/path.6373 · Cited by 8 · see citing works
de Lima, R. C., & Quaresma, J. A. S. (2025) · Computers · doi:10.3390/computers14050202 · Cited by 11 · see citing works
Pocevičiūtė, M., Eilertsen, G., Garvin, S., & Lundström, C. F. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43904-9_16 · Cited by 8 · see citing works
Xiong, Y., Gao, Y., Qi, Y., Zhi, Y., Xu, J., Wang, K., Yang, Q., Wang, C., Zhao, M., & Meng, X. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-02869-0 · Cited by 7 · see citing works
Stellungnahme „Künstliche Intelligenz in der Medizin“
MWR (2025) · Deutsches Ärzteblatt · doi:10.3238/arztebl.2025.stellungnahme_ki_medizin · Cited by 8 · see citing works
The Rise of AI-Assisted Diagnosis: Will Pathologists Be Partners or Bystanders?
El‐Khoury, R., & Zaatari, G. S. (2025) · Diagnostics · doi:10.3390/diagnostics15182308 · Cited by 10 · see citing works
Zhu, M. S., Wang, G., Su, F., Liu, Y., Wang, Y., Yang, J., Luo, J., Cao, F., Zhen, P., Huang, B., et al. (2023) · American Journal Of Pathology · doi:10.1016/j.ajpath.2023.03.012 · Cited by 7 · see citing works
Vazzano, J. L., Johansson, D., Hu, K., Eurén, K., Elfwing, S., Parwani, A. V., & Zhou, M. (2023) · Laboratory Investigation · doi:10.1016/j.labinv.2023.100257 · Cited by 7 · see citing works
Applications of artificial intelligence to myeloproliferative neoplasms: a narrative review
Srisuwananukorn, A., Krull, J. E., Ma, Q., Zhang, P., Pearson, A. T., & Hoffman, R. (2024) · Expert Review of Hematology · doi:10.1080/17474086.2024.2389997 · Cited by 7 · see citing works
Bioinformatics in urology — molecular characterization of pathophysiology and response to treatment
Gheinani, A. H., Kim, J., You, S., & Adam, R. M. (2023) · Nature Reviews Urology · doi:10.1038/s41585-023-00805-3 · Cited by 9 · see citing works
Deep Learning Predicts EGFR Mutation Status from Histology Images in Non–Small Cell Lung Cancer
Park, J., Shin, S., Hwang, W., Keum, S., Brattoli, B., Rawson, J. H., Lee, T., Pereira, S., Ahn, C. H., Senior, M. J. T., et al. (2025) · Cancer Research Communications · doi:10.1158/2767-9764.crc-25-0155 · Cited by 6 · see citing works
How Does Medical Artificial Intelligence Revolutionize Physician Productivity?
Lim, J.-Y., Kim, K.-H., & Mun, S. (2025) · Yonsei Medical Journal · doi:10.3349/ymj.2025.0389 · Cited by 7 · see citing works
Mateos-Aparicio-Ruiz, I., Pedraza, A., Becker, J. U., Altini, N., Salido, J., & Bueno, G. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.11.049 · Cited by 6 · see citing works
- Authors
- da Silva, L. M., Pereira, E. M., Salles, P. G., Godrich, R., Ceballos, R., Kunz, J. D., Casson, A., Viret, J., Chandarlapaty, S., Ferreira, C. G., Ferrari, B., Rothrock, B., Raciti, P., Reuter, V., Dogdas, B., DeMuth, G., Sue, J., Kanan, C., Grady, L., Fuchs, T. J., & Reis‐Filho, J. S.
- Affiliations
- Grupo Oncoclinicas Sao Paulo Brazil
- Instituto Mario Penna Belo Horizonte Brazil
- Paige New York NY USA
- Memorial Sloan Kettering Cancer Center (US)
- Stat One Wilmington NC USA
- Department of Pathology Memorial Sloan Kettering Cancer Center New York NY USA
- Corresponding author
- Fuchs, Thomas J
- ORCID
- Venue
- The Journal of Pathology
- Publisher
- Wiley
- Published
- 2021-04-27
- Volume
- 254
- Issue
- 2
- Pages
- 147-158
- ISSN
- 0022-3417 (ISSN-L)
- 1096-9896
- PMC
- PMC8252036
- OpenAlex
- W3157209223
- Cited by
- 175
- References
- 21
- Citations by year
- 2026: 21 · 2025: 53 · 2024: 46 · 2023: 35 · 2022: 15 · 2021: 5
- FWCI
- 8.88
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- The Journal of Pathology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Breast Cancer Research Foundation
- National Cancer Institute (P30CA008748, K12CA184746)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Prostate Cancer Diagnosis and Treatment
- Keywords
- prostate cancer detection, automated histopathology, whole slide images, artificial intelligence, diagnostic accuracy, diagnostic efficiency, prostate atrophy, adenosis, diagnostic variability, pathologist shortage, central pathology review, real-world validation
- MeSH terms
- Machine Learning, Pathologists, Aged, Aged, 80 and over, Artificial Intelligence, Biopsy, Humans, Male, Middle Aged, Prostate, Prostatic Neoplasms, Biopsy, Large-Core Needle
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