Digital pathology and artificial intelligence

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812

Unpaired image-to-image translation using cycle-consistent adversarial networks

Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.244 · Cited by 23,602

GAN（Generative Adversarial Nets）

淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 20,507

The cancer genome atlas pan-cancer analysis project

The Cancer Genome Atlas Research Network, Weinstein, J. N., Collisson, E. A., Mills, G. B., Shaw, K. R. M., Ozenberger, B. A., Ellrott, K., Shmulevich, I., Sander, C., & Stuart, J. M. (2013) · Nature Genetics · doi:10.1038/ng.2764 · Cited by 9,092

Conditional Generative Adversarial Nets

Mirza, M., & Osindero, S. (2014) · arXiv · doi:10.48550/arxiv.1411.1784 · Cited by 8,863

Image Style Transfer Using Convolutional Neural Networks

Gatys, L. A., Ecker, A. S., & Bethge, M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.265 · Cited by 6,025

Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning

Coudray, N., Ocampo, P. S., Sakellaropoulos, T., Narula, N., Snuderl, M., Fenyö, D., Moreira, A. L., Razavian, N., & Tsirigos, A. (2018) · Nature Medicine · doi:10.1038/s41591-018-0177-5 · Cited by 2,930

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial NetworksPreprint

Zhu, J.-Y., Park, T., Isola, P., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.10593 · Cited by 2,278

Superior transplant recipient outcome prediction and pathology assessment using rapid deep learning applied to procurement kidney biopsies

Gaut, J. P., Marsh, J. N., Chen, L., Zhang, F., Yu, X., Wilson, P. C., Liu, T., & Swamidass, S. J. (2025) · Scientific Reports · doi:10.1038/s41598-025-31667-x · Cited by 2

Seamless full-field imaging with uniform resolution via wavevector-compensated Fourier ptychography

Feng, T., Wang, A., Xu, J., Gao, H., Hui, D., Li, X., & Pan, A. (2025) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2025.109549 · Cited by 2

Graph Neural Network‐Based Multi‐Scale Whole Slide Image Fusion for pT Staging of Muscle‐Invasive Bladder Cancer

Li, Q., Chen, Q., & Liao, N. Q. (2025) · Cancer Science · doi:10.1111/cas.70292 · Cited by 2

Enhancing early prediction of pathological complete response in breast cancer using attention-based convolutional neural networks in digital pathology

Comes, M. C., Lupo, A., Bozzi, A., Fanizzi, A., Cirillo, A., Nunzio, G. D., Pastena, M. I., Rizzo, A., Guven, D. C., Vitale, E., et al. (2026) · Digital Health · doi:10.1177/20552076261419242 · Cited by 2

Artificial Intelligence‐Based Pathological Subtype Diagnosis of Nasal Polyps: A Multidimensional and Micro‐Visualization Study

Luo, X., Li, H., Chen, J., Zhou, Z., Liang, G., Chao, M., Yuan, T., Zhang, H., Kang, N., Xu, Z., et al. (2026) · Allergy · doi:10.1111/all.70282 · Cited by 2

Digital pathology platforms with integrated AI algorithms: a structured landscape analysis and recommendations for clinical implementation

Winter, L., Artinger, A., Albin, J., Cotarelo, C., & Brochhausen, C. (2026) · Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin · doi:10.1007/s00428-026-04480-8 · Cited by 2

Ensemble based in transfer learning for cytological classification in pleural fluid

López-Córdova, F., Vega-Huerta, H., Maquen-Niño, G. L. E., Cáceres-Pizarro, J., Rodríguez, C., Calderón, D., Moreno, J. G., de Villa, P. E. D. L. C. V., Guerra-Grados, L., Henríquez, S. D. M., et al. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1849898 · Cited by 2

Artificial intelligence in diagnostics and medical decision making

Levi, M. M. (2026) · European Journal of Internal Medicine · doi:10.1016/j.ejim.2026.107056 · Cited by 2

Recent technical advances in whole slide imaging instrumentation

Katare, P., & Gorthi, S. S. (2021) · Journal of Microscopy · doi:10.1111/jmi.13049 · Cited by 2

Eosinophil Detection with Modified YOLOv3 Model in Large Pathology Image

Wang, X., Che, Y., Jiang, N., Chen, W., & Lan, L. (2021) · International Conference on Computer Information Science and Artificial Intelligence (CISAI) · doi:10.1109/cisai54367.2021.00019 · Cited by 2

Pilot study of fluorescence imitating brightfield imaging for rapid, slide‐free dermatopathology

Engel, T. N., Abraham, T. M., Morningstar, T., Fung, M. A., Rangchi, A., Kiuru, M. H., Fereidouni, F., & Levenson, R. M. (2022) · Journal of Cutaneous Pathology · doi:10.1111/cup.14308 · Cited by 2

Dynamic Channel Selection in Self-Supervised Learning

Krishna, T., Rai, A., Djilali, Y. A. D., Smeaton, A. F., McGuinness, K., & O'Connor, N. E. (2022) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.56541/lkli8696 · Cited by 2

Artificial Intelligence in Nephrology

da Silva, A. Q. B., Almeida, J., & Kirsztajn, G. M. (2022) · Springer eBooks · doi:10.1007/978-3-031-11570-7_15 · Cited by 2

Molecular, Genetic, and Other Omics Data

Meystre, S. M., Gouripeddi, R., & Alekseyenko, A. V. (2023) · Health informatics · doi:10.1007/978-3-031-27173-1_16 · Cited by 2

The Impact of COVID-19 on Chronic Pain: Multidimensional Clustering Reveals Deep Insights into Spinal Cord Stimulation Patients

Berger, S., Agurto, C., Cecchi, G., Eyigöz, E., Hershey, B., Lechleiter, K. M., Huynh, D., McDonald, M., & Rogers, J. L. (2023) · IEEE International Conference on Digital Health (ICDH) · doi:10.1109/icdh60066.2023.00023 · Cited by 2

Multi-Class Breast Cancer Classification from Digital Mammograms Using Vision Transformers

Salgarkar, V. S., & Ilavarasi, A. K. (2023) · International Conference on Contemporary Computing · doi:10.1145/3607947.3608043 · Cited by 2

Deep Cellular Embeddings: An Explainable Plug and Play Improvement for Feature Representation in Histopathology

Gildenblat, J., Yüce, A., Abbasi-Sureshjani, S., & Korski, K. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43987-2_75 · Cited by 2

Mapping the Literature of Artificial Intelligence in Medical Education: A Scientometric Analysis

Maulana, F. I., Zaın, M. Y., Lestari, D., Purnomo, A., & Adi, P. D. P. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-99-5166-6_47 · Cited by 2

Making AI’s Impact on Pathology Visible: Using Ethnographic Methods for Ethical and Epistemological Insights

Milota, M., Drogt, J., & Jongsma, K. R. (2023) · Digital Society · doi:10.1007/s44206-023-00072-0 · Cited by 2

Enhancing students’ digital skills through a Biotechnology & Bioprocessing module designed for chemical engineers

Fernández‐Castané, A. (2024) · Cogent Education · doi:10.1080/2331186x.2023.2299201 · Cited by 2

Review of the potential benefits and challenges of artificial intelligence in clinical laboratory

Tiwade, Y. R., Bankar, N. J., Mishra, V. H., & Sajjanar, A. (2024) · Journal of Cellular Biotechnology · doi:10.3233/jcb-230119 · Cited by 2

Sliding Window Optimal Transport for Open World Artifact Detection in Histopathology

Fuchs, M., Konstantin, M., Schrade, N., Schweizer, L., Tolkach, Y., & Mukhopadhyay, A. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3383590 · Cited by 2

Democratizing Artificial Intelligence in Anatomic Pathology

Flotte, T. J., Derauf, S. A., Byrd, R. K., Kroneman, T. N., Bell, D. A., Stetzik, L. A., Lee, S., Samiei, A., Hart, S. N., García, J. J., et al. (2024) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2023-0205-oa · Cited by 2

Comparative analysis of chronic progressive nephropathy (CPN) diagnosis in rat kidneys using an artificial intelligence deep learning model

Bae, Y., Byun, J., Lee, H., & Han, B. (2024) · Toxicological Research · doi:10.1007/s43188-024-00247-y · Cited by 3

Signal Detection in Three-Dimensional Confocal Microscopy Images Through Deep Learning

Paulik, R., Kozlovszky, M., & Molnár, B. (2024) · IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI) · doi:10.1109/saci60582.2024.10619912 · Cited by 2

Supporting Mitosis Detection AI Training with Inter-Observer Eye-Gaze Consistencies

Gu, H., Yan, Z., Alvi, A., Day, B., Yang, C., Wu, Z., Magaki, S., Haeri, M., & Chen, X. (2024) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi61247.2024.00013 · Cited by 2

Assessment of knowledge, attitude, and practice regarding artificial intelligence in histopathology: A cross-sectional study among oral pathologists in India

Indu, M., Shankar, V. G., Cherian, L. M., Krishna, R., Paul, S., & Sathyan, P. (2022) · Saudi Journal of Oral Sciences · doi:10.4103/sjoralsci.sjoralsci_19_22 · Cited by 2

Tumor specific protein 70 targeted tumor cell isolation technology can improve the accuracy of cytopathological examination

Zhang, L., Zhou, Y., Yang, S., Zhu, Q., Xu, J., Yuan, M., Gu, C., Ju, H., Rong, R., & Pan, S. (2025) · Clinical Chemistry and Laboratory Medicine (CCLM) · doi:10.1515/cclm-2024-0878 · Cited by 2

Deep-Learning-Based Prediction of t(11;14) in Multiple Myeloma H&E-Stained Samples

Kerner, N., Hershkovitz, D., Trestman, S., Shragai, T., Nahmias, H. L., Cohen, Y. C., Ziv‐Baran, T., & Avivi, I. M. (2025) · Cancers · doi:10.3390/cancers17111733 · Cited by 2

Diagnostic challenges of faded hematoxylin and eosin slides: limitations of re-staining and re-sectioning and possible reason to go digital

Odate, T., Lami, K., Tsuyama, N., Mori, I., Kiriyama, Y., Teramoto, N., Masuzawa, Y., Sukhbaatar, O., Masui, K., Yoon, H., et al. (2025) · Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin · doi:10.1007/s00428-025-04209-z · Cited by 2

The Future of Ocular Pathobiology

Shu, D. Y. (2025) · American Journal Of Pathology · doi:10.1016/j.ajpath.2025.09.003 · Cited by 2

Texture improvement for human shape estimation from a single image

Escribano, J. G., Rauno, S., Swaminathan, A., Smyth, D., & Smolić, A. (2022) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.56541/soww6683 · Cited by 2

Multimodal Data Analysis Using Soft Computing Techniques For The Detection and Classification of Lung Cancer

Vinod, C., & Menaka, D. (2022) · International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT) · doi:10.1109/icicict54557.2022.9917865 · Cited by 2

Recognition of Image One Feature Point Using Convolutional Neural Networks

Hori, M., Jincho, M., Tadasuke, H. O. R. I., SEKINE, H., Kato, A., Ueno, A., & Kawai, T. (2021) · Journal of Hard Tissue Biology · doi:10.2485/jhtb.30.161 · Cited by 2

Artificial intelligence in pathological evaluation of gastrointestinal cancers

Alpsoy, A., Yavuz, A., & Elpek, G. Ö. (2021) · Artificial Intelligence in Gastroenterology · doi:10.35712/aig.v2.i6.141 · Cited by 2

Predicting HER2 scores from registered HER2 and H&E images

Tavolara, T. E., Niazi, M. K. K., Tozbikian, G. H., Wesolowski, R., & Gürcan, M. N. (2022) · SPIE Medical Imaging: Digital and Computational Pathology · doi:10.1117/12.2612878 · Cited by 2

Unsupervised Scale-Invariant Multispectral Shape Matching

Pazi, I., Ginzburg, D., & Raviv, D. (2022) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.56541/vhmq4826 · Cited by 5

Klinische Entscheidungsunterstützung in der Kardiologie

Radke, P. W. (2023) · Die Kardiologie · doi:10.1007/s12181-023-00600-9 · Cited by 2

Precise localization of microvascular invasion in hepatocellular carcinoma based on three-dimensional histology-MR image fusion: an ex vivo experimental study

Li, L., Chen, J., Huang, Y., Wu, C., Ye, D., Wu, W., Zhou, X., Qin, P., Jia, T., Lin, Y., et al. (2023) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-23-220 · Cited by 2

Ethics of Artificial Intelligence: Implications for Primary Care and Family Medicine Residency Programs

Wojda, T. R., Hoffman, C., Kindler, K., Desai, A., & Visweswaran, S. (2024) · Artificial intelligence · doi:10.5772/intechopen.114907 · Cited by 2

PathEX: Make good choice for whole slide image extraction

Yang, X., Zhang, R., Yang, Y., Zhang, Y., & Chen, K. (2024) · PLoS ONE · doi:10.1371/journal.pone.0304702 · Cited by 2

Foundation models and information retrieval in digital pathology

Tizhoosh, H. R. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95359-7.00011-x · Cited by 2

Outlook of the future landscape of artificial intelligence in medicine and new challenges

Xing, L., Kapp, D. S., Giger, M. L., & Min, J. K. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-821259-2.00025-9 · Cited by 2

Brain Immunoinformatics: A Symmetrical Link between Informatics, Wet Lab and the Clinic

Papageorgiou, I. E., Bittner, D., Psychogios, M., & Hadjidemetriou, S. (2021) · Symmetry · doi:10.3390/sym13112168 · Cited by 2

Setting Standards to Promote Artificial Intelligence in Colon Mass Endoscopic SamplingPreprint

Zheng, Y., Su, R., Wang, W., Meng, S., Xiao, H., Zhang, W., Xu, H., Bu, Y., Zhong, Y., Zhang, Y., et al. (2019) · medRxiv · doi:10.1101/19008078 · Cited by 2

Bayesian Landmark-based Shape Analysis of Tumor Pathology ImagesPreprint

Zhang, C., Xiao, G., Moon, C., Chen, M., & Li, Q. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.01149 · Cited by 2

Automated Scoring of Nuclear Pleomorphism Spectrum with Pathologist-level Performance in Breast CancerPreprint

Mercan, C., Balkenhol, M. C. A., Salgado, R., Sherman, M. E., Vielh, P. R., Vreuls, W., Polónia, A., Horlings, H. M., Weichert, W., Carter, J. M., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.04974 · Cited by 2

Renal Cell Carcinoma Whole-Slide Image Classification and Search Using Deep LearningPreprint

Safarpoor, A., Shafiei, S., Gonzalez, R., Parwani, A. V., & Tizhoosh, H. R. (2021) · Research Square · doi:10.21203/rs.3.rs-971708/v1 · Cited by 2

Digital and Computational Pathology: What a Time to Be Alive!

Berbis, M. A. (2023) · Mayo Clinic Proceedings Digital Health · doi:10.1016/j.mcpdig.2023.11.006 · Cited by 2

Machine learning enabled prediction of digital biomarkers from whole slide histopathology imagesPreprint

McCaw, Z. R., Shcherbina, A., Shah, Y., Huang, D., Elliott, S. E., Szabó, P. M., Dulken, B., Holland, S. J., Tagari, P. C., Light, D., et al. (2024) · medRxiv · doi:10.1101/2024.01.06.24300926 · Cited by 2

Yemeni university students public perceptions toward the use of artificial intelligence in healthcare: A cross-sectional studyPreprint

Hatem, N. A. H., Ibrahim, M. I. M., & Yousuf, S. A. (2024) · medRxiv · doi:10.1101/2024.02.27.24303457 · Cited by 2

Assessment of Knowledge, Attitudes, and Practices in Artificial Intelligence Among Healthcare Professionals in Mogadishu, SomaliaPreprint

Ahmed, M. M., Dirie, N. I., Mohamud, A. K., Garba, B., Mohamed, Z. O., Ali, I., Mohamed, A. A., Ali, A. N., Hassan, M. M., Asowe, H. A., et al. (2024) · Research Square · doi:10.21203/rs.3.rs-4359643/v1 · Cited by 3

Pathologist-interpretable breast cancer subtyping and stratification from AI-inferred nuclear featuresPreprint

Barman, R. K., Dhruba, S. R., Hoang, D.-T., Shulman, E. D., Campagnolo, E. M., Wang, A. T., Harmon, S. A., Hu, T. C., Papanicolau‐Sengos, A., Nasrallah, M. P., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.04.674077 · Cited by 2

Artificial Intelligence (AI) in Pathology – A Summary and Challenges

Buch, A. C., & Kulkarni, R. (2021) · Global Journal of Medical Research · doi:10.34257/gjmrkvol21is2pg23 · Cited by 3

Artificial Intelligence, Bioinformatics, and Pathology

Levy, J., Lu, Y., Montivero, M. K. S., Ramwala, O. A., McFadden, J. R., Miles, C., Diamond, A. G., Reddy, R., Reddy, R., Hudson, T. R., et al. (2022) · Advances in Molecular Pathology · doi:10.1016/j.yamp.2023.01.002 · Cited by 1

A COMPREHENSIVE REVIEW OF INTEGRATING AI AND MEDICAL IMAGING IN LUNG CANCER DIAGNOSIS

Mostafavi, M., & Shabani, M. (2023) · doi:10.53555//jptcp.v31i3.4929 · Cited by 3

Breast Cancer Detection and Classification Using Artificial Intelligence

Pravalika, L., Bachu, S., & Kumar, N. U. (2024) · International Conference on Innovative Sustainable Technologies for Energy, Mechatronics, and Smart Systems (ISTEMS) · doi:10.1109/istems60181.2024.10560257 · Cited by 1

StainSegNet: Stain Normalization and Segmentation-Driven Colorectal Tumor Tissues Classification

Ponnarasee, B. K., Lalithamani, N., & Ajibesin, A. A. (2023) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci62032.2023.00196 · Cited by 1

An Immunofluorescence-Guided Segmentation Model in Hematoxylin and Eosin Images Is Enabled by Tissue Artifact Correction Using a Cycle-Consistent Generative Adversarial Network

Wiedenmann, M., Barch, M., Chang, P. S., Giltnane, J. M., Risom, T., & Zijlstra, A. (2024) · Modern Pathology · doi:10.1016/j.modpat.2024.100591 · Cited by 1

Optimization of whole slide imaging scan settings for computer vision using human lung cancer tissue

Geubbelmans, M., Claes, J., Nijsten, K., Gervois, P., Appeltans, S., Martens, S., Wolfs, E., Thomeer, M., Valkenborg, D., & Faes, C. (2024) · PLoS ONE · doi:10.1371/journal.pone.0309740 · Cited by 1

Pathomics signatures and cuproptosis-related genes signatures for prediction of prognosis in patients with hepatocellular carcinoma

Li, X., Li, L., He, N., Kou, D., Chen, S., Song, H. K., & Yan, X. (2024) · Translational Cancer Research · doi:10.21037/tcr-24-350 · Cited by 1

Advancing Laboratory Medicine: A Review of Emerging Technologies and Their Impact on Diagnostic Accuracy and Efficiency

Alabdali, D. H., Alsolami, E. A., AlSehli, O. O., Aldawsari, H. H., Alzain, B. M., Yamani, O. F., Alsharif, R. N., Endergiri, D. I. M., Harthi, A. S. A., & Alamoudi, A. H. (2024) · Journal of Ecohumanism · doi:10.62754/joe.v3i8.4856 · Cited by 1

Diagnostic Value of Artificial Intelligence-Based Pathology Diagnosis System in Lymphatic Metastasis of Gastric Cancer

Zhao, T., Wu, Q., Zhu, C., Ma, H., & Da, M. (2024) · Oncology · doi:10.1159/000542852 · Cited by 1

KonvLiNA: integrating Kolmogorov-Arnold Network with linear Nyström attention for feature fusion in crop field detection

Haruna, Y., Qin, S., Lawan, A., & Chukkol, A. H. A. (2025) · International Conference on Machine Vision · doi:10.1117/12.3055084 · Cited by 1

Instruction tuning for colorectal histopathology: a multimodal vision-language assistant on human-evaluated data

Afzaal, U., Su, Z., Sajjad, U., Stack, T., Rezapour, M., Lü, H., Niu, S., Gürcan, M. N., Frankel, W. L., Chen, W. R., et al. (2025) · SPIE Medical Imaging: Digital and Computational Pathology · doi:10.1117/12.3047352 · Cited by 1

Liver biopsy and acute hepatology in intensive care unit patients

Mätzold, L., Roßner, F., & Tacke, F. (2025) · Expert Review of Gastroenterology & Hepatology · doi:10.1080/17474124.2025.2521346 · Cited by 1

Development of a national pathology training system using digital pathology and SNOMED-CT

McGenity, C., Cratchley, A. L., Aldridge, J., Sayers, C., Campbell, W. S., Dash, R. C., Flanagan, A. M., Sebire, N. J., Wright, A. I., Akhtar, W., et al. (2025) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2025.100456 · Cited by 1

Construction of an oligometastatic prediction model for nasopharyngeal carcinoma patients based on pathomics features and dynamic multi-swarm particle swarm optimization support vector machine

Li, Y., Zhang, D., Wang, Y., Hu, Y., Wen, Z., Yang, C., Zhou, P., & Cheng, W. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1589919 · Cited by 1

Role of Telepathology, Artificial Intelligence, and Emerging Technologies in Enhancing Diagnostic Accuracy

Tiwade, Y. R., Noman, O., Dawande, P. P., Bankar, N. J., Bahadure, S. D., & Patil, P. S. (2025) · Journal of Nature and Science of Medicine · doi:10.4103/jnsm.jnsm_80_24 · Cited by 1

Construction of prognostic scoring model for ovarian cancer based on deep learning algorithm

Zhong, X., Xiao, H., Lu, W., Chen, J., Chao, F., & Tu, R. (2025) · Discover Oncology · doi:10.1007/s12672-025-03011-2 · Cited by 1

Resident Physicians’ Perceptions of Artificial Intelligence and Implications for Medical Education: A Qualitative Study

McFarlane, A., Sadri, S., Schwartz, E., & Gowda, D. (2025) · MedEdPublish · doi:10.12688/mep.20833.1 · Cited by 1

Deep µStitch: an unsupervised sliding window-based deep global microscopy image stitching framework

Wang, S., & Yu, W. (2025) · Applied Optics · doi:10.1364/ao.564775 · Cited by 1

Predicting the Recurrence of Sporadic Odontogenic Keratocyst Using Whole‐Slide Histopathology Images With the Hybrid Encoder Iterative Attention Convolution Model

Mohanty, S., Shivanna, D. B., Rao, R. S., & Astekar, M. S. (2025) · Clinical and Experimental Dental Research · doi:10.1002/cre2.70184 · Cited by 1

MIE: Magnification-integrated ensemble method for improving glomeruli segmentation in medical imaging

Han, Y., Kim, J., Park, S., Moon, J.-S., & Lee, J.-H. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.109041 · Cited by 1

BPMambaMIL: A bio-inspired prototype-guided multiple instance learning for oncotype DX risk assessment in histopathology

Guo, Y., Su, Z., Koyun, O. C., Lü, H., Wesolowski, R., Tozbikian, G. H., Niazi, M. K. K., & Gürcan, M. N. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.109039 · Cited by 4

Analysis of system and scanner downtime in a digital pathology–predominant institution: A 6-year experience

Reagans, R., Cevik, L., Kumar, H., Kellough, D. A., Limbach, A. L., Lujan, G. M., Parwani, A. V., & Gokozan, H. N. (2025) · American Journal of Clinical Pathology · doi:10.1093/ajcp/aqaf094 · Cited by 1

Enhanced prediction of gene mutation and risk stratification in non-small-cell lung cancer through dual-pathway fusion of radiomics and pathomics

Ji, Y., Ge, R., Meng, W., Yang, X., Lu, H., Lu, B., Zheng, C., Teng, Y., Pan, H., Liu, L., et al. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1646851 · Cited by 1

Multiharmonic imaging-based automated recognition of cutaneous T-cell lymphoma

Ghosh, S., Pavlova, O., Latshaw, A., Kim, D., Iselin, C., Bernard, P., Prompsy, P., Chang, Y., Staedler, D., Tsai, Y.-C., et al. (2025) · British Journal of Dermatology · doi:10.1093/bjd/ljaf345 · Cited by 1

Deep learning-based recurrent laryngeal nerve detection during endoscopic thyroidectomy

Yang, S., Hua, S., Wang, Z., Zheng, H., Gao, J., Lü, P., Hua, Y., Chen, G., Liu, Q., Liu, W., et al. (2025) · Intelligent Medicine · doi:10.1016/j.imed.2025.08.005 · Cited by 1

Segment Anything Model for Gastric Cancer

Li, L., Wang, C., Geng, Y., Chen, T., Wang, Z., Lin, K., Wei, H., Wang, J., Wang, D., Niu, D., et al. (2025) · Cancer Medicine · doi:10.1002/cam4.71246 · Cited by 1

A Fast Path from Innovation to Safe and Effective Medicines

Arlett, P. R., Ehmann, F., Herold, R., Korakianiti, E., Moscariello, P., Barbier, L., Gravanis, I., Pétavy, F., Cormier, E., Thomson, A. L., et al. (2025) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.70107 · Cited by 1

A Review on the Applications of Artificial Intelligence for Diagnosing and Treating Cervical Cancer

Wu, Y., & Wang, A. (2025) · International Journal of Women s Health · doi:10.2147/ijwh.s551976 · Cited by 2

H&E to Oncotype DX: Predicting recurrence risk in HR+/HER2- breast cancer

Koyun, O. C., Guo, Y., Su, Z., Lü, H., Rezapour, M., Wesolowski, R., Tozbikian, G. H., Niazi, M. K. K., & Gürcan, M. N. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.130469 · Cited by 1

PathoEye: A deep learning framework for the whole-slide image analysis of skin tissue

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Interpretable machine learning for urothelial cells classification and risk scoring in urine cytology

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Interdisciplinary Synergy in Translational Medicine: Overcoming Collaboration Barriers Between Basic Researchers, Clinicians, and Industry Stakeholders

Judijanto, L. (2025) · European Journal of Applied Science Engineering and Technology · doi:10.59324/ejaset.2025.3(6).10 · Cited by 1

A pathomics model for predicting response to chemo-immunotherapy in lung squamous cell carcinoma: A multicenter study

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The Robustness of Deep Learning Models to Adversarial Attacks in Lung X-ray Classification

Li, X., Pang, Y., & Li, Y. (2025) · International Conference on Machine Learning, Pattern Recognition and Automation Engineering (MLPRAE) · doi:10.1109/mlprae67267.2025.11290762 · Cited by 3

KonvLiNA: integrating Kolmogorov-Arnold network with linear Nyström attention for feature fusion in crop disease detection

Haruna, Y. G., Qin, S., Lawan, A., Chukkol, A. H. A., & Abdulhamid, I. H. (2026) · Multimedia Systems · doi:10.1007/s00530-025-02126-4 · Cited by 1

Artificial intelligence driven image recognition in medical education: current applications, educational impact, challenges, and future directions

Andreas, S., Pana, Z. D., Larentzakis, A. V., Kondylis, S., Maina, A. M., Ziogas, N. K., & Ntourakis, D. (2026) · Global Surgical Education - Journal of the Association for Surgical Education · doi:10.1007/s44186-025-00411-4 · Cited by 2

Radiopathomics for prediction of early recurrence after curative resection in patients with hepatocellular carcinoma

Zhang, D.-M., Xiang, F., Wang, Y., Li, X.-M., Zhang, X.-C., Zhou, Y.-S., Liu, J., & Fan, Y. (2026) · Hepatobiliary & pancreatic diseases international · doi:10.1016/j.hbpd.2025.10.011 · Cited by 1

Multimodal Phasor Analysis for Digital Pathology: Quantitative Characterization of Liver Iron Overload

Panzeri, D., Akpınar, R., D’Alfonso, L., Bouzin, M., Presotto, L., Collini, M., Chirico, G. G., Tommaso, L. D., Inverso, D., & Sironi, L. (2026) · Chemical & Biomedical Imaging · doi:10.1021/cbmi.5c00229 · Cited by 1

Generalisation of automatic tumour segmentation in histopathological whole-slide images across multiple cancer types

Skrede, O.-J., Pradhan, M., Isaksen, M. X., Hveem, T. S., Vlatkovic, L., Nesbakken, A., Lindemann, K. Y., Kristensen, G. B., Kasius, J. C., Zeimet, A. G., et al. (2026) · npj Precision Oncology · doi:10.1038/s41698-026-01311-6 · Cited by 1

Ensuring reliable digital pathology: a comparative analysis of HistoQC and PathProfiler for artefacts detection in prostate whole-slide images

Ravanelli, D., Robbi, E., Citter, S., Barbareschi, M., & Trianni, A. (2026) · Physica Medica · doi:10.1016/j.ejmp.2026.105745 · Cited by 1

Holopath-VR360-degree virtual reality videos in pathology education: The Holopath-VR experience

Alcaraz‐Mateos, E., Abellan-Leante, I., Galdame, F. S., García-Hidalgo, C., Perez-La-Encina, M., Caballero‐Alemán, F., & Sánchez-Campoy, N. (2026) · Revista Española de Patología · doi:10.1016/j.patol.2026.100861 · Cited by 1

Deep learning–driven immunohistochemical analysis of renal lymphatics for chronic kidney disease: bioinformatic and histopathological study

Xu, X., Lin, Y., Pei, G., Zeng, R., & Xu, G. (2026) · Journal of Pathology and Translational Medicine · doi:10.4132/jptm.2025.12.15 · Cited by 1

Morphology-Aware Prognostic Model for Five-Year Survival Prediction in Colorectal Cancer from H&E Whole-Slide Images: A Study Using Multi-Center Clinical Trial Cohort

Sajjad, U., Akbar, A., Su, Z., Leyva, A., Knight, D. A., Frankel, W. L., Gürcan, M. N., Chen, W., & Niazi, M. K. K. (2026) · Cancers · doi:10.3390/cancers18071150 · Cited by 9

Advances in multi-omics research on neuroblastoma

Wang, Y., Zhao, Z., Wang, J., Song, S., Zhao, C., Qv, C., & Lu, H. (2026) · Frontiers in Pediatrics · doi:10.3389/fped.2026.1739376 · Cited by 1

Perineural invasion in solid tumors: biological foundations and the emerging integration of machine learning and artificial intelligence

Bahmad, H. F., Mirzabeigi, Y., Elajami, Y. K., Abou-Kheir, W. G., & Nair, N. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1801005 · Cited by 1

Data‐driven thresholds for standardized classification of severe Alzheimer's disease neuropathology using digital neuropathology

Shahidehpour, R. K., Neltner, A. M., Klusty, M. A., Corbett, W. C., Gonzalez, A. D., Gutman, D. A., Fardo, D. W., Bachstetter, A. D., Bumgardner, C., Flanagan, M. E., et al. (2026) · Brain Pathology · doi:10.1111/bpa.70102 · Cited by 1

AI and the digital pathology revolution: clinical applications in cancer diagnosis and assessment

Paul, S., Isaacs, H. M., & Côté, R. J. (2026) · Expert Review of Molecular Diagnostics · doi:10.1080/14737159.2026.2665801 · Cited by 3

RELEVANCE OF AI IN ORAL PATHOLOGY

George, D. J., Vijayareka, K., & Nithya, S. (2026) · Iterative International Publishers eBooks · doi:10.58532/nbennuaertv6b1c34

Artificial Intelligence and Digital Pathology Synergy: For detailed, accurate and predictive analysis of WSIs

Ankit, & Hasija, Y. (2022) · International Conference on Advanced Computing and Communication Systems (ICACCS) · doi:10.1109/icaccs54159.2022.9785034 · Cited by 3

Artificial intelligence in diagnostic pathology

Shafi, S., & Parwani, A. V. (2023) · Diagnostic Pathology · doi:10.1186/s13000-023-01375-z · Cited by 371

Artificial Intelligence and Digital Pathology: A Narrative Review on Advancements and Opportunities for Improved Diagnosis and Treatment

Dhamapurkar, M. U., Barbade, R. D., Meghana, S., Kulkarni, S., & Godge, P. (2024) · JOURNAL OF CLINICAL AND DIAGNOSTIC RESEARCH · doi:10.7860/jcdr/2024/67706.19538 · Cited by 1