Artificial Intelligence for Dermatopathology: Current Trends and the Road Ahead

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works

Long Short-Term Memory

Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works

U-Net: Convolutional Networks for Biomedical Image Segmentation

Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · see citing works

Deep learning

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

Generative adversarial networks

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2020) · Communications of the ACM · doi:10.1145/3422622 · Cited by 18,384 · see citing works

Dermatologist-level classification of skin cancer with deep neural networks

Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362 · see citing works

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

Rudin, C. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0048-x · Cited by 10,561 · see citing works

High-performance medicine: the convergence of human and artificial intelligence

Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450 · see citing works

Transformers: State-of-the-Art Natural Language Processing

Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., et al. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · doi:10.18653/v1/2020.emnlp-demos.6 · Cited by 8,741 · see citing works

A guide to deep learning in healthcare

Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0316-z · Cited by 5,312 · see citing works

Receiver Operating Characteristic Curve in Diagnostic Test Assessment

Mandrekar, J. N. (2010) · Journal of Thoracic Oncology · doi:10.1097/jto.0b013e3181ec173d · Cited by 4,506 · see citing works

Generative Adversarial Networks

Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1406.2661 · Cited by 4,477 · see citing works

Residual Attention Network for Image Classification

Wang, F., Jiang, M., Qian, C., Yang, S., Li, C., Zhang, H., Wang, X., & Tang, X. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.683 · Cited by 4,073 · see citing works

The future of digital health with federated learning

Rieke, N., Hancox, J., Li, W., Milletarì, F., Roth, H. R., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B. A., Maier-Hein, K., et al. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00323-1 · Cited by 3,119 · see citing works

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 · 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

Deep Clustering for Unsupervised Learning of Visual Features

Caron, M., Bojanowski, P., Joulin, A., & Douze, M. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01264-9_9 · Cited by 2,674 · see citing works

The false hope of current approaches to explainable artificial intelligence in health care

Ghassemi, M., Oakden-Rayner, L., & Beam, A. L. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00208-9 · Cited by 1,724 · see citing works

Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology

Bera, K., Schalper, K. A., Rimm, D. L., Velcheti, V., & Madabhushi, A. (2019) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-019-0252-y · Cited by 1,698 · see citing works

Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data

Sheller, M. J., Edwards, B., Reina, G. A., Martin, J., Pati, S., Kotrotsou, A., Milchenko, M., Xu, W., Marcus, D., Colen, R. R., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-69250-1 · Cited by 1,646 · see citing works

Predicting cancer outcomes from histology and genomics using convolutional networks

Mobadersany, P., Yousefi, S., Amgad, M., Gutman, D. A., Barnholtz-Sloan, J. S., Velázquez Vega, J. E., Brat, D. J., & Cooper, L. A. D. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1717139115 · Cited by 1,084 · see citing works

Weakly-and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation

Papandreou, G., Chen, L.-C., Murphy, K. P., & Yuille, A. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.203 · Cited by 1,041 · see citing works

Transfusion: Understanding Transfer Learning for Medical ImagingPreprint

Raghu, M., Zhang, C., Kleinberg, J. M., & Samy Bengio, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.07208 · Cited by 845 · see citing works

Federated learning for predicting clinical outcomes in patients with COVID-19

Dayan, I., Roth, H. R., Zhong, A., Harouni, A., Gentili, A., Abidin, A. Z., Liu, A., Costa, A. B., Wood, B. J., Tsai, C.-S., et al. (2021) · Nature Medicine · doi:10.1038/s41591-021-01506-3 · Cited by 842 · see citing works

Pan-cancer image-based detection of clinically actionable genetic alterations

Kather, J. N., Heij, L. R., Grabsch, H. I., Loeffler, C. M. L., Echle, A., Muti, H. S., Krause, J., Niehues, J., Sommer, K. A. J., Bankhead, P., et al. (2020) · Nature Cancer · doi:10.1038/s43018-020-0087-6 · Cited by 660 · see citing works

AI-based pathology predicts origins for cancers of unknown primary

Lu, M. Y., Chen, T. Y., Williamson, D. F. K., Zhao, M., Shady, M., Lipkova, J., & Mahmood, F. (2021) · Nature · doi:10.1038/s41586-021-03512-4 · Cited by 636 · see citing works

Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast Cancer

Steiner, D. F., MacDonald, R., Liu, Y., Truszkowski, P., Hipp, J. D., Gammage, C., Thng, F., Peng, L., & Stumpe, M. C. (2018) · American Journal of Surgical Pathology · doi:10.1097/pas.0000000000001151 · Cited by 489 · see citing works

Artificial intelligence-based image classification methods for diagnosis of skin cancer: Challenges and opportunities

Goyal, M., Knackstedt, T., Yan, S., & Hassanpour, S. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104065 · Cited by 461 · see citing works

Pathologists’ diagnosis of invasive melanoma and melanocytic proliferations: observer accuracy and reproducibility study

Elmore, J. G., Barnhill, R. L., Elder, D. E., Longton, G., PEPE, M. S., Reisch, L. M., Carney, P. A., Titus, L. J., Nelson, H. D., Onega, T., et al. (2017) · BMJ · doi:10.1136/bmj.j2813 · Cited by 449 · see citing works

Artificial intelligence for good health: a scoping review of the ethics literature.

Murphy, K., Di Ruggiero, E., Upshur, R., Willison, D. J., Malhotra, N., Cai, J. C., Malhotra, N., Lui, V., & Gibson, J. (2021) · BMC Medical Ethics · doi:10.1186/s12910-021-00577-8 · Cited by 435 · see citing works

A Deep Learning Architecture for Image Representation, Visual Interpretability and Automated Basal-Cell Carcinoma Cancer Detection

Cruz-Roa, A. A., Arevalo Ovalle, J. E., Madabhushi, A., & González Osorio, F. A. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-40763-5_50 · Cited by 403 · see citing works

Artificial intelligence and the implementation challenge

Shaw, J., Rudzicz, F., Jamieson, T., & Goldfarb, A. (2019) · Journal of Medical Internet Research · doi:10.2196/13659 · Cited by 389 · see citing works

Artificial Intelligence and Machine Learning in Pathology: The Present Landscape of Supervised Methods

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

Association between surgical skin markings in dermoscopic images and diagnostic performance of a deep learning convolutional neural network for melanoma recognition

Winkler, J. K., Fink, C., Toberer, F., Enk, A., Deinlein, T., Hofmann-Wellenhof, R., Thomas, L., Lallas, A., Blum, A., Stolz, W., et al. (2019) · JAMA Dermatology · doi:10.1001/jamadermatol.2019.1735 · Cited by 365 · see citing works

An artificial intelligence algorithm for prostate cancer diagnosis in whole slide images of core needle biopsies: a blinded clinical validation and deployment study

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

Impact of a deep learning assistant on the histopathologic classification of liver cancer

Kiani, A., Uyumazturk, B., Rajpurkar, P., Wang, A., Gao, R., Jones, E., Yu, Y., Langlotz, C. P., Ball, R. L., Montine, T. J., et al. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0232-8 · Cited by 325 · see citing works

Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images

Hekler, A., Utikal, J. S., Enk, A. H., Solass, W., Schmitt, M., Klode, J., Schadendorf, D., Sondermann, W., Franklin, C., Bestvater, F., et al. (2019) · European Journal of Cancer · doi:10.1016/j.ejca.2019.06.012 · Cited by 285 · see citing works

Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations

Chan, S. Y., Reddy, V. D., Myers, B. A., Thibodeaux, Q. G., Brownstone, N. D., & Liao, W. J. (2020) · Dermatology and Therapy · doi:10.1007/s13555-020-00372-0 · Cited by 275 · see citing works

Federated learning for computational pathology on gigapixel whole slide images

Lu, M. Y., Chen, R. J., Kong, D., Lipkova, J., Singh, R., Williamson, D. F. K., Chen, T. Y., & Mahmood, F. (2022) · Medical Image Analysis · doi:10.1016/j.media.2021.102298 · Cited by 267 · see citing works

Deep learning-based survival prediction for multiple cancer types using histopathology images

Wulczyn, E., Steiner, D. F., Xu, Z., Sadhwani, A., Wang, H., Flament-Auvigne, I., Mermel, C. H., Chen, P.-H. C., Liu, Y., & Stumpe, M. C. (2020) · PLOS ONE · doi:10.1371/journal.pone.0233678 · Cited by 266 · see citing works

Federated learning improves site performance in multicenter deep learning without data sharing

Sarma, K. V., Harmon, S., Sanford, T., Roth, H. R., Xu, Z., Tetreault, J., Xu, D., Flores, M. G., Raman, A. G., Kulkarni, R., et al. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa341 · Cited by 242 · see citing works

Pathologist-level classification of histopathological melanoma images with deep neural networks

Hekler, A., Utikal, J. S., Enk, A. H., Berking, C., Klode, J., Schadendorf, D., Jansen, P., Franklin, C., Holland-Letz, T., Krahl, D., et al. (2019) · European Journal of Cancer · doi:10.1016/j.ejca.2019.04.021 · Cited by 231 · see citing works

Interpretable survival prediction for colorectal cancer using deep learning

Wulczyn, E., Steiner, D. F., Moran, M., Plass, M., Reihs, R., Tan, F., Flament-Auvigne, I., Brown, T., Regitnig, P., Chen, P.-H. C., et al. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00427-2 · Cited by 231 · see citing works

Development and Validation of a Deep Learning Algorithm for Gleason Grading of Prostate Cancer From Biopsy Specimens

Nagpal, K., Foote, D., Tan, F. E., Liu, Y., Chen, P.-H. C., Steiner, D. F., Manoj, N. S., Olson, N. H., Smith, J. L., Mohtashamian, A., et al. (2020) · JAMA Oncology · doi:10.1001/jamaoncol.2020.2485 · Cited by 223 · see citing works

Deep reinforcement learning in medical imaging: A literature review

Zhou, S., Le, N., Luu, K., Nguyen, H. V., & Ayache, N. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102193 · Cited by 222 · see citing works

Efficient deep learning model for mitosis detection using breast histopathology images

Saha, M., Chakraborty, C., & Racoceanu, D. (2017) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2017.12.001 · Cited by 196 · see citing works

Validation of a digital pathology system including remote review during the COVID-19 pandemic

Hanna, M. G., Reuter, V. E., Ardon, O., Kim, D., Sirintrapun, S. J., Schüffler, P. J., BUSAM, K. J., Sauter, J. L., Brogi, E., Tan, L. K., et al. (2020) · Modern Pathology · doi:10.1038/s41379-020-0601-5 · Cited by 187 · see citing works

Novel artificial intelligence system increases the detection of prostate cancer in whole slide images of core needle biopsies

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 · see citing works

Using Machine Learning Algorithms to Predict Immunotherapy Response in Patients with Advanced Melanoma

Johannet, P. M., Coudray, N., Donnelly, D. M., Jour, G., Illa-Bochaca, I., Xia, Y., Johnson, D. B., Wheless, L., Patrinely, J. R., Nomikou, S., et al. (2020) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-20-2415 · Cited by 181 · see citing works

Automatic detection of mycobacterium tuberculosis using artificial intelligence

Xiong, Y., Ba, X., Hou, A., Zhang, K., Chen, L., & Li, T. (2018) · Journal of Thoracic Disease · doi:10.21037/jtd.2018.01.91 · Cited by 162 · 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

Quality control stress test for deep learning-based diagnostic model in digital pathology

Schömig-Markiefka, B., Pryalukhin, A., Hulla, W., Bychkov, A., Fukuoka, J., Madabhushi, A., Achter, V., Nieroda, L., Büttner, R., Quaas, A., et al. (2021) · Modern Pathology · doi:10.1038/s41379-021-00859-x · Cited by 146 · see citing works

High-Throughput, Machine Learning-Based Quantification of Steatosis, Inflammation, Ballooning, and Fibrosis in Biopsies from Patients with Nonalcoholic Fatty Liver Disease

Forlano, R., Mullish, B. H., Giannakeas, N., Maurice, J. B., Angkathunyakul, N., Lloyd, J., Tzallas, A. T., Tsipouras, M., Yee, M., Thursz, M. R., et al. (2020) · Clinical Gastroenterology and Hepatology · doi:10.1016/j.cgh.2019.12.025 · Cited by 142 · see citing works

Artificial intelligence applications in dermatology: Where do we stand?

Gomolin, A., Netchiporouk, E., Gniadecki, R., & Litvinov, I. V. (2020) · Frontiers in Medicine · doi:10.3389/fmed.2020.00100 · Cited by 142 · see citing works

Point-of-Care Digital Cytology With Artificial Intelligence for Cervical Cancer Screening in a Resource-Limited Setting

Holmström, O., Linder, N., Kaingu, H., Mbuuko, N., Mbete, J., Kinyua, F., Törnquist, S., Muinde, M., Krogerus, L. A., Lundin, M., et al. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2021.1740 · Cited by 134 · see citing works

Deep Learning Based on Standard H&E Images of Primary Melanoma Tumors Identifies Patients at Risk for Visceral Recurrence and Death

Kulkarni, P. M., Robinson, E. J., Pradhan, J. S., Gartrell, R. D., Rohr, B. R., Trager, M. H., Geskin, L. J., Kluger, H. M., Wong, P. F., Ács, B., et al. (2019) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-19-1495 · Cited by 126 · see citing works

Evaluation of the Use of Combined Artificial Intelligence and Pathologist Assessment to Review and Grade Prostate Biopsies

Steiner, D. F., Nagpal, K., Sayres, R., Foote, D., Wedin, B., Pearce, A., Cai, C. J., Winter, S. R., Symonds, M., Yatziv, L., et al. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.23267 · Cited by 126 · see citing works

Challenges in the Development, Deployment, and Regulation of Artificial Intelligence in Anatomic Pathology

Cheng, J., Abel, J. T., Balis, U. J., McClintock, D. S., & Pantanowitz, L. (2020) · American Journal Of Pathology · doi:10.1016/j.ajpath.2020.10.018 · Cited by 126 · see citing works

An Advanced Deep Learning Approach for Ki-67 Stained Hotspot Detection and Proliferation Rate Scoring for Prognostic Evaluation of Breast Cancer

Saha, M., Chakraborty, C., Arun, I., Ahmed, R., & Chatterjee, S. (2017) · Scientific Reports · doi:10.1038/s41598-017-03405-5 · Cited by 121 · see citing works

Diagnostic Performance of Deep Learning Algorithms Applied to Three Common Diagnoses in Dermatopathology

Olsen, T. G., Jackson, B. H., Feeser, T. A., Kent, M. N., Moad, J. C., Krishnamurthy, S., Lunsford, D. D., & Soans, R. E. (2018) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_31_18 · Cited by 119 · see citing works

A multi-phase deep CNN based mitosis detection framework for breast cancer histopathological images

Sohail, A., Khan, A., Wahab, N., Zameer, A., & Khan, S. (2021) · Scientific Reports · doi:10.1038/s41598-021-85652-1 · Cited by 117 · see citing works

Trust me, i'm a chatbot: how artificial intelligence in health care fails the Turing Test

Powell, J. (2019) · Journal of Medical Internet Research · doi:10.2196/16222 · Cited by 115 · see citing works

Tailored for Real-World: A Whole Slide Image Classification System Validated on Uncurated Multi-Site Data Emulating the Prospective Pathology Workload

Ianni, J. D., Soans, R. E., Sankarapandian, S., Chamarthi, R. V., Ayyagari, D., Olsen, T. G., Bonham, M. J., Stavish, C. C., Motaparthi, K., Cockerell, C. J., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-59985-2 · Cited by 113 · see citing works

NuSeT: A deep learning tool for reliably separating and analyzing crowded cells

Yang, L., Ghosh, R. P., Franklin, J. M., Chen, S., You, C., Narayan, R. R., Melcher, M. L., & Liphardt, J. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008193 · Cited by 101 · see citing works

An unsupervised feature learning framework for basal cell carcinoma image analysis

Arévalo, J., Cruz-Roa, Á., Arias, V., Romero, E., & Gonzalez, F. A. (2015) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2015.04.004 · Cited by 99 · see citing works

Deep learning enables pathologist-like scoring of NASH models

Heinemann, F., Birk, G., & Stierstorfer, B. (2019) · Scientific Reports · doi:10.1038/s41598-019-54904-6 · Cited by 96 · see citing works

Artificial intelligence in dermatopathology: diagnosis, education, and research

Wells, A., Patel, S., Lee, J. B., & Motaparthi, K. (2021) · Journal of Cutaneous Pathology · doi:10.1111/cup.13954 · Cited by 95 · see citing works

Closing the translation gap: AI applications in digital pathology

Steiner, D. F., Chen, P.-H. C., & Mermel, C. H. (2020) · Biochimica et Biophysica Acta (BBA) - Reviews on Cancer · doi:10.1016/j.bbcan.2020.188452 · Cited by 89 · see citing works

Artificial Intelligence and Mapping a New Direction in Laboratory Medicine: A Review

Herman, D. S., Rhoads, D. D., Schulz, W. L., & Durant, T. J. S. (2021) · Clinical Chemistry · doi:10.1093/clinchem/hvab165 · Cited by 84 · see citing works

Introduction to Artificial Intelligence and Machine Learning for Pathology

Harrison, J. H., Gilbertson, J. R., Hanna, M. G., Olson, N. H., Seheult, J. N., Sorace, J. M., & Stram, M. N. (2021) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2020-0541-cp · Cited by 83 · see citing works

MaskMitosis: a deep learning framework for fully supervised, weakly supervised, and unsupervised mitosis detection in histopathology images

Sebai, M., Wang, X., & Wang, T. (2020) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-020-02175-z · Cited by 82 · see citing works

Deep learning identifies morphological features in breast cancer predictive of cancer ERBB2 status and trastuzumab treatment efficacy

Bychkov, D., Linder, N., Tiulpin, A., Kücükel, H., Lundin, M., Nordling, S., Sihto, H. J., Isola, J. J., Lehtimäki, T., Kellokumpu‐Lehtinen, P., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-83102-6 · Cited by 76 · see citing works

Psoriasis skin biopsy image segmentation using Deep Convolutional Neural Network

Pal, A., Garain, U., Chandra, A., Chatterjee, R., & Senapati, S. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.01.027 · Cited by 74 · see citing works

Recognizing basal cell carcinoma on smartphone‐captured digital histopathology images with a deep neural network

Jiang, Y., Xiong, J., Li, H. Y., Yang, X., Yu, W. T., Gao, M., Zhao, X., Ma, Y., Zhang, W., Guan, Y. F., et al. (2019) · British Journal of Dermatology · doi:10.1111/bjd.18026 · Cited by 72 · see citing works

Classification of melanocytic lesions in selected and whole-slide images via convolutional neural networks

Hart, S. N., Flotte, W., Norgan, A. F., Shah, K. K., Buchan, Z. R., Mounajjed, T., & Flotte, T. J. (2019) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_32_18 · Cited by 69 · see citing works

Deep learning for dermatologists: Part II. Current applications

Puri, P., Comfere, N. I., Drage, L. A., Shamim, H., Bezalel, S. A., Pittelkow, M. R., Davis, M. D. P., Wang, M., Mangold, A. R., Tollefson, M. M., et al. (2020) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2020.05.053 · Cited by 67 · see citing works

The Internet of Federated Things (IoFT)

Kontar, R. A., Shi, N., Yue, X., Chung, S., Byon, E., Chowdhury, M., Jin, J. J., Kontar, W., Masoud, N., Nouiehed, M., et al. (2021) · IEEE Access · doi:10.1109/access.2021.3127448 · Cited by 67 · see citing works

Deeply Supervised UNet for Semantic Segmentation to Assist Dermatopathological Assessment of Basal Cell Carcinoma

Arrastia, J. L., Heilenkötter, N., Baguer, D. O., Hauberg‐Lotte, L., Boskamp, T., Hetzer, S., Duschner, N., Schaller, J., & Maaß, P. (2021) · Journal of Imaging · doi:10.3390/jimaging7040071 · Cited by 66 · see citing works

Comparative analysis of machine learning approaches to classify tumor mutation burden in lung adenocarcinoma using histopathology images

Sadhwani, A., Chang, H.-W., Behrooz, A., Brown, T., Flament-Auvigne, I., Patel, H., Findlater, R., Velez, V., Tan, F. E., Tekiela, K., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-95747-4 · Cited by 60 · see citing works

Unlocking the Power of Artificial Intelligence and Big Data in Medicine

Lovis, C. (2019) · Journal of Medical Internet Research · doi:10.2196/16607 · Cited by 58 · see citing works

A deep learning approach for mitosis detection: Application in tumor proliferation prediction from whole slide images

Nateghi, R., Danyali, H., & Helfroush, M. S. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102048 · Cited by 56 · see citing works

A Deep Learning Approach for Histopathological Diagnosis of Onychomycosis: Not Inferior to Analogue Diagnosis by Histopathologists

Decroos, F., Springenberg, S., Lang, T., Päpper, M., Zapf, A., Metze, D., Steinkraus, V, & Böer-Auer, A. (2021) · Acta Dermato Venereologica · doi:10.2340/00015555-3893 · Cited by 51 · see citing works

Biological data annotation via a human-augmenting AI-based labeling system

van der Wal, D., Jhun, I., Laklouk, I. A., Nirschl, J., Richer, L., Rojansky, R. B., Theparee, T., Wheeler, J., Sander, J., Feng, F. Y.-C., et al. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00520-6 · Cited by 43 · see citing works

Artificial intelligence for advance requesting of immunohistochemistry in diagnostically uncertain prostate biopsies

Chatrian, A., Colling, R. T., Browning, L., Alham, N. K., Sirinukunwattana, K., Malacrino, S., Haghighat, M., Aberdeen, A., Monks, A., Moxley-Wyles, B., et al. (2021) · Modern Pathology · doi:10.1038/s41379-021-00826-6 · Cited by 41 · see citing works

The Real Era of the Art of Medicine Begins with Artificial Intelligence

Meskó, B. (2019) · Journal of Medical Internet Research · doi:10.2196/16295 · Cited by 37 · see citing works

Artificial Intelligence in Dermatopathology: New Insights and Perspectives

Cazzato, G., Colagrande, A., Cimmino, A., Arezzo, F., Loizzi, V., Caporusso, C., Marangio, M., Foti, C., Romita, P., Lospalluti, L., et al. (2021) · Dermatopathology · doi:10.3390/dermatopathology8030044 · Cited by 37 · see citing works

Artificial intelligence for prediction of donor liver allograft steatosis and early post-transplantation graft failure

Narayan, R. R., Abadilla, N., Yang, L., Chen, S., Klinkachorn, M., Eddington, H., Trickey, A. W., Higgins, J., & Melcher, M. L. (2021) · HPB · doi:10.1016/j.hpb.2021.10.004 · Cited by 34 · see citing works

A Pathology Deep Learning System Capable of Triage of Melanoma Specimens Utilizing Dermatopathologist Consensus as Ground Truth

Sankarapandian, S., Kohn, S., Spurrier, V., Grullon, S., Soans, R. E., Ayyagari, K. D., Chamarthi, R. V., Motaparthi, K., Lee, J. B., Shon, W., et al. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00076 · Cited by 29 · see citing works

Automated skin biopsy histopathological image annotation using multi-instance representation and learning

Zhang, G., Yin, J. P., Li, Z., Su, X., Li, G., & Zhang, H. (2013) · BMC Medical Genomics · doi:10.1186/1755-8794-6-s3-s10 · Cited by 24 · see citing works

Interpretable classification from skin Cancer histology slides using deep learning: A retrospective multicenter study

Xie, P., Zuo, K., Zhang, Y., Li, F., Yin, M., & Lu, K. (2019) · arXiv · doi:10.48550/arxiv.1904.06156 · Cited by 23 · see citing works

Deep-learning based classification distinguishes sarcomatoid malignant mesotheliomas from benign spindle cell mesothelial proliferations

Naso, J. R., Levine, A., Farahani, H., Chirieac, L. R., Đačić, S., Wright, J. L., Lai, C. K., Yang, H.-M., Jones, S. J. M., Bashashati, A., et al. (2021) · Modern Pathology · doi:10.1038/s41379-021-00850-6 · Cited by 23 · see citing works

Multi-magnification-based machine learning as an ancillary tool for the pathologic assessment of shaved margins for breast carcinoma lumpectomy specimens

D’Alfonso, T. M., Ho, D. J., Hanna, M. G., Grabenstetter, A., Yarlagadda, D. V. K., Geneslaw, L., Ntiamoah, P., Fuchs, T. J., & Tan, L. K. (2021) · Modern Pathology · doi:10.1038/s41379-021-00807-9 · Cited by 22 · see citing works

Impact of Concurrent Use of Artificial Intelligence Tools on Radiologists Reading Time: A Prospective Feasibility Study

Müller, F. C., Raaschou, H., Akhtar, N., Brejnebøl, M. W., Collatz, L., & Andersen, M. B. (2021) · Academic Radiology · doi:10.1016/j.acra.2021.10.008 · Cited by 22 · see citing works

Differential evolution based advised SVM for histopathalogical image analysis for skin cancer detection

Masood, A., & Al-Jumaily, A. A. (2015) · doi:10.1109/embc.2015.7318478 · Cited by 21 · see citing works

Artificial Intelligence in Nonalcoholic Fatty Liver Disease: A New Frontier in Diagnosis and Treatment

Aggarwal, P., & Alkhouri, N. (2021) · Clinical Liver Disease · doi:10.1002/cld.1071 · Cited by 21 · see citing works

Apprehensions about Excessive Belief in Digital Therapeutics: Points of Concern Excluding Merits

Kim, H. (2020) · Journal of Korean Medical Science · doi:10.3346/jkms.2020.35.e373 · Cited by 20 · see citing works

A Review of Unsupervised Artificial Neural Networks with Applications

Fabiyi, S. D. (2019) · International Journal of Computer Applications · doi:10.5120/ijca2019918425 · Cited by 19 · see citing works

Semantic Segmentation of Histopathological Slides for the Classification of Cutaneous Lymphoma and Eczema

Scheurer, J., Ferrari, C., Berenguer Todo Bom, L., Beer, M., Kempf, W., & Haug, L. (2020) · Communications in Computer and Information Science · doi:10.1007/978-3-030-52791-4_3 · Cited by 18 · see citing works

Augmenting Multi-Instance Multilabel Learning with Sparse Bayesian Models for Skin Biopsy Image Analysis

Zhang, G., Yin, J. P., Su, X., Huang, Y., Lao, Y., Liang, Z., Ou, S., & Zhang, H. (2014) · BioMed Research International · doi:10.1155/2014/305629 · Cited by 17 · see citing works

Independent validation of paige prostate: Assessing clinical benefit of an artificial intelligence tool within a digital diagnostic pathology laboratory workflow.

Kanan, C., Sue, J., Grady, L. J., Fuchs, T. J., Chandarlapaty, S., Reis-Filho, J. S., Salles, P. G. O., Silva, L. M. D., Ferreira, C. G., & Pereira, E. M. (2020) · Journal of Clinical Oncology · doi:10.1200/jco.2020.38.15_suppl.e14076 · Cited by 13 · see citing works

Will Artificial Intelligence Translate Big Data Into Improved Medical Care or Be a Source of Confusing Intrusion? A Discussion Between a (Cautious) Physician Informatician and an (Optimistic) Medical Informatics Researcher

Zeng‐Treitler, Q., & Nelson, S. J. (2019) · Journal of Medical Internet Research · doi:10.2196/16272 · Cited by 12 · see citing works

Deep Learning for the Digital Pathologic Diagnosis of Cholangiocarcinoma and Hepatocellular Carcinoma: Evaluating the Impact of a Web-based Diagnostic AssistantPreprint

Uyumazturk, B., Kiani, A., Rajpurkar, P., Wang, A. Y., Ball, R. L., Gao, R. W., Yu, Y., Jones, E., Langlotz, C. P., Martin, B. A., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.07372 · Cited by 11 · see citing works

Efficient cellular annotation of histopathology slides with real-time AI augmentation

Diao, J. A., Chen, R. J., & Kvedar, J. C. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00534-0 · Cited by 9 · see citing works

Re: Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images

Géraud, C., & Griewank, K. (2019) · European Journal of Cancer · doi:10.1016/j.ejca.2019.09.018 · Cited by 6 · see citing works

Artificial intelligence in the detection of skin cancer

Beltrami, E. J., Brown, A. C., SALMON, P. J., Leffell, D. J., Ko, J., & Grant-Kels, J. M. (2022) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2022.08.028 · Cited by 77 · see citing works

Advancing cancer diagnosis and treatment: integrating image analysis and AI algorithms for enhanced clinical practice

Saeidnia, H. R., Firuzpour, F., Kozak, M., & majd, H. S. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11117-w · Cited by 47 · see citing works

Deep learning in computational dermatopathology of melanoma: A technical systematic literature review

Sauter, D., Lodde, G. C., Nensa, F., Schadendorf, D., Livingstone, E., & Kukuk, M. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107083 · Cited by 33 · see citing works

Artificial intelligence in digital pathology of cutaneous lymphomas: A review of the current state and future perspectives

Doeleman, T., Hondelink, L. M., Vermeer, M. H., van Dijk, M. R., & Schrader, A. M. R. (2023) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2023.06.004 · Cited by 26 · see citing works

Evaluation of a Deep Learning Approach to Differentiate Bowen’s Disease and Seborrheic Keratosis

Jansen, P., Baguer, D. O., Duschner, N., Arrastia, J. L., Schmidt, M., Wiepjes, B., Schadendorf, D., Hadaschik, E. N., Maaß, P., Schaller, J., et al. (2022) · Cancers · doi:10.3390/cancers14143518 · Cited by 26 · see citing works

Ethics of artificial intelligence in dermatology

Chen, M., Zhou, A. E., Jain, N. P., Gronbeck, C., Feng, H., & Grant-Kels, J. M. (2024) · Clinics in Dermatology · doi:10.1016/j.clindermatol.2024.02.003 · Cited by 25 · see citing works

Artificial Intelligence Applied to a First Screening of Naevoid Melanoma: A New Use of Fast Random Forest Algorithm in Dermatopathology

Cazzato, G., Massaro, A., Colagrande, A., Trilli, I., Ingravallo, G., Casatta, N., Lupo, C., Ronchi, A., Franco, R., Maiorano, E., et al. (2023) · Current Oncology · doi:10.3390/curroncol30070452 · Cited by 18 · see citing works

Transforming Dermatopathology With AI: Addressing Bias, Enhancing Interpretability, and Shaping Future Diagnostics

Kakish, D. R. K., AlSamhori, J. F., Fajardo, A. N. R., Qaqish, L. N., Jaber, L. A., Abujudeh, R., Al-Zuriqat, M., Mohammed, A. Y., & Nashwan, A. J. (2025) · Dermatological Reviews · doi:10.1002/der2.70018 · Cited by 14 · see citing works

Artificial intelligence for nonmelanoma skin cancer

Trager, M. H., Gordon, E. R., Breneman, A., Weng, C., & Samie, F. H. (2024) · Clinics in Dermatology · doi:10.1016/j.clindermatol.2024.06.016 · Cited by 11 · see citing works

MAC-ResNet: Knowledge Distillation Based Lightweight Multiscale-Attention-Crop-ResNet for Eyelid Tumors Detection and Classification

Huang, X., Yao, C., Xu, F., Chen, L., Wang, H., Chen, X.-D., Ye, J., & Wang, Y. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm13010089 · Cited by 10 · see citing works

Artificial Intelligence in Surgery, Surgical Subspecialties, and Related Disciplines

Lee, R. Y., Ziehm, A. I., Ullrich, L. A., & Stawicki, S. P. (2023) · Artificial intelligence · doi:10.5772/intechopen.112691 · Cited by 10 · see citing works

Artificial intelligence in autoimmune bullous dermatoses

Manuelyan, K., Dragolov, M., Drenovska, K., Shahid, M., & Vassileva, S. (2024) · Clinics in Dermatology · doi:10.1016/j.clindermatol.2024.06.008 · Cited by 9 · see citing works

The State of Artificial Intelligence in Skin Cancer Publications

Joly-Chevrier, M., Nguyen, A. X.-L., Liang, L., Lesko-Krleza, M., & Lefrançois, P. (2024) · Journal of Cutaneous Medicine and Surgery · doi:10.1177/12034754241229361 · Cited by 9 · see citing works

Artificial intelligence in genitourinary pathology

Patel, A., Parwani, A. V., & Satturwar, S. P. (2025) · Histopathology · doi:10.1111/his.70020 · Cited by 9 · see citing works

Improving the Diagnosis of Skin Biopsies Using Tissue Segmentation

Nofallah, S., Li, B., Mokhtari, M., Wu, W., Knezevich, S. R., May, C. J., Chang, O. H., Elmore, J. G., & Shapiro, L. G. (2022) · Diagnostics · doi:10.3390/diagnostics12071713 · Cited by 7 · see citing works

Artificial Intelligence in the Histopathological Assessment of Non-Neoplastic Skin Disorders: A Narrative Review with Future Perspectives

Della Mura, M., Sorino, J., Colagrande, A., Daruish, M., Ingravallo, G., Massaro, A., Cazzato, G., Lupo, C., Casatta, N., Ribatti, D., et al. (2025) · Medical Sciences · doi:10.3390/medsci13020070 · Cited by 6 · see citing works

Differential Effects of Metformin on Immune-Mediated and Androgen-Mediated Non-Cancer Skin Diseases in Diabetes Patients: A Retrospective Cohort Study

Tseng, C. (2023) · Dermatology · doi:10.1159/000530077 · Cited by 6 · see citing works

A call for implementing augmented intelligence in pediatric dermatology

Issa, C. J., Reimer‐Taschenbrecker, A., & Paller, A. S. (2023) · Pediatric Dermatology · doi:10.1111/pde.15298 · Cited by 5 · see citing works

Revolutionizing diagnostic pathology: The emergence and impact of artificial intelligence—what doesn't kill you makes you stronger?

Alhatem, A., Wong, T., & Lambert, W. C. (2024) · Clinics in Dermatology · doi:10.1016/j.clindermatol.2023.12.020 · Cited by 5 · see citing works

Ethical concerns related to the use of artificial intelligence in dermatopathology

Cockerell, C. J., & Goldust, M. (2024) · International Journal of Dermatology · doi:10.1111/ijd.17223 · Cited by 5 · see citing works

A Review of Artificial Intelligence in Dermatopathology

Prestwood, C. A., Gibbs, D. C., Wolner, Z. J., & Stoff, B. K. (2025) · Dermatologic Clinics · doi:10.1016/j.det.2025.05.006 · Cited by 3 · see citing works

Enhancing Early Detection of Metastatic Cutaneous Squamous Cell Carcinoma (CSCC): Integrating AI With Histopathological Assessments

Bramer, E. M., Chia, C. M., Rentroia‐Pacheco, B., Tokez, S., Pijnenborg, L., Damman, J. J., Amir, A. L., Kumar, D., Hollestein, L. M., Mooyaart, A. L., et al. (2025) · Experimental Dermatology · doi:10.1111/exd.70135 · Cited by 2 · see citing works

Advances in artificial intelligence in prostate cancer pathology

Chan, G. J., & Ding, C. C. (2026) · Seminars in Diagnostic Pathology · doi:10.1016/j.semdp.2026.150995 · Cited by 2 · see citing works

Emerging Applications of Artificial Intelligence in Dermatopathology

Smith, M. P., & Schulman, J. M. (2024) · Current Dermatology Reports · doi:10.1007/s13671-024-00431-1 · Cited by 2 · see citing works

Ethical, legal, and regulatory challenges in AI-based healthcare tools

Dompmartin, A., & Haykal, D. (2026) · La Presse Médicale · doi:10.1016/j.lpm.2026.104372 · Cited by 1 · see citing works

Practical applications of computational pathology: bridging technology and clinical practice

Hanna, M. G., Rashidi, H., & Pantanowitz, L. (2026) · Pathology · doi:10.1016/j.pathol.2026.06.012 · Cited by 1 · see citing works

Prognosis prediction of high grade serous adenocarcinoma based on multi-modal convolution neural network

Liao, X., Li, L., Gan, Z., Li, K., & Zheng, X. W. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-09231-3 · Cited by 1 · see citing works

Updates in Dermatopathology: Emerging Trends in Diagnosis and Classification

Hussain, E. A. A. (2023) · International Journal on Recent and Innovation Trends in Computing and Communication · doi:10.17762/ijritcc.v11i8.9435 · Cited by 1 · see citing works

The use of a deep learning model in the histopathological diagnosis of actinic keratosis: A case control accuracy studyPreprint

Balkenhol, J., Schmidt, M., Schnauder, T., Langhorst, J., Arrastia, J. L., Baguer, D. O., Gilbert, G., Schmitz, L., & Dirschka, T. (2023) · medRxiv · doi:10.1101/2023.11.20.23298649 · Cited by 1 · see citing works

Editorial 3/07

McLean, M. (2007) · Current Oncology · doi:10.3747/co.2007.115

The Terry Fox Research Institute’s West Coast Dialogue: Are We Prepared for Personalized Medicine?

Curwin, K., Smith, C., & Sutcliffe, S. B. J. (2010) · Current Oncology · doi:10.3747/co.v17i6.768

Digital dermatopathology implementation: Experience at a multisite academic institution

Proffer, S. L., Reinhart, J. P., Ridgeway, J. L., Barry, B. A., Kamath, C. C., Gerdes, E. O. W., Todd, A., Cervenka, D. J., DiCaudo, D. J., Sokumbi, O., et al. (2024) · Journal of Cutaneous Pathology · doi:10.1111/cup.14629

Research on Facial Expression Diagnosis Based on Deep Learning

Li, W., Chen, Y., Wu, S., & Wang, X. (2024) · International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) · doi:10.1109/ainit61980.2024.10581769

A deep learning model for histopathological diagnosis of actinic keratosis: a diagnostic case control accuracy study

Balkenhol, J., SCHMIDT, M., Schnauder, T., Langhorst, J., Arrastia, J. L., Baguer, D. O., Gilbert, G., Lacarrubba, F., Dirschka, T., & Schmitz, L. (2026) · Italian Journal of Dermatology and Venereology · doi:10.23736/s2784-8671.25.08336-7

Part III : Digital Dermatopathology Playbook: Change Management, AI Integration, and Strategic Lessons Beyond Technology

Sokumbi, O., Velusamy, B., Peters, M. S., & Comfere, N. I. (2026) · Journal of Cutaneous Pathology · doi:10.1111/cup.70194

Patient-level hyperspectral state-space learning for melanoma pathology diagnosis

Zhao, Q., Lei, S., Tian, C., & Li, W. (2026) · Photodiagnosis and Photodynamic Therapy · doi:10.1016/j.pdpdt.2026.105642

Artificial Intelligence in the Diagnosis of Atopic Dermatitis

Dermitzakis, I., Sotiriou, E., Vakirli, I., Kyriakoudi, S. A., Karakus, H., Gregoriou, S., Lallas, A., & Vakirlis, E. (2026) · Dermatology Practical & Conceptual · doi:10.5826/dpc.160s1a7537

COVER (PMS 180&K) (p/u from previous issue w/updates)

(2022) · Seminars in Diagnostic Pathology · doi:10.1053/s0740-2570(22)00031-4

Update on immunohistochemical techniques in dermatopathology. A focus on adnexal neoplasms.

Duran, J., & Plaza, J. A. (2022) · Seminars in Diagnostic Pathology · doi:10.1053/j.semdp.2022.01.001 · Cited by 2 · see citing works

The BoneXpert method for automatic determination of bone age and bone health from hand radiographs

Thodberg, H. H., & Martin, D. D. (2009) · Bone · doi:10.1016/j.bone.2009.04.190

Artificial intelligence in dermatopathology

Bhullar, P. K., Murphree, D. H., Choudhary, A., Peters, M. S., Sokumbi, O., & Comfere, N. I. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-15688-5.00050-4

Artificial intelligence: its role in dermatopathology

Jartarkar, S. R. (2022) · Indian Journal of Dermatology Venereology and Leprology · doi:10.25259/ijdvl_725_2021 · Cited by 18 · see citing works