The DOI matches its Crossref record; 243 of the 243 references it cites were found in a registry or an index.
Generative Artificial Intelligence in Anatomic Pathology
Open accessAbstract
Context.—: Generative artificial intelligence (AI) has emerged as a transformative force in various fields, including anatomic pathology, where it offers the potential to significantly enhance diagnostic accuracy, workflow efficiency, and research capabilities. Objective.—: To explore the applications, benefits, and challenges of generative AI in anatomic pathology, with a focus on its impact on diagnostic processes, workflow efficiency, education, and research. Data Sources.—: A comprehensive review of current literature and recent advancements in the application of generative AI within anatomic pathology, categorized into unimodal and multimodal applications, and evaluated for clinical utility, ethical considerations, and future potential. Conclusions.—: Generative AI demonstrates significant promise in various domains of anatomic pathology, including diagnostic accuracy enhanced through AI-driven image analysis, virtual staining, and synthetic data generation; workflow efficiency, with potential for improvement by automating routine tasks, quality control, and reflex testing; education and research, facilitated by AI-generated educational content, synthetic histology images, and advanced data analysis methods; and clinical integration, with preliminary surveys indicating cautious optimism for nondiagnostic AI tasks and growing engagement in academic settings. Ethical and practical challenges require rigorous validation, prompt engineering, federated learning, and synthetic data generation to help ensure trustworthy, reliable, and unbiased AI applications. Generative AI can potentially revolutionize anatomic pathology, enhancing diagnostic accuracy, improving workflow efficiency, and advancing education and research. Successful integration into clinical practice will require continued interdisciplinary collaboration, careful validation, and adherence to ethical standards to ensure the benefits of AI are realized while maintaining the highest standards of patient care.
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“The synthetic region-level captions can easily be generated with the generative AI model to provide a wealth of text guidance88,127.”
From A multimodal whole-slide foundation model for pathology (2025) · cites this paper as [127]
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Cites
Items 201–243 of 243
Cervical cytopathology image refocusing via multi-scale attention features and domain normalization
Geng, X., Liu, X., Cheng, S., & Zeng, S. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102566 · Cited by 14
Whole Slide Image Understanding in Pathology: What Is the Salient Scale of Analysis?
Jenkinson, E., & Arandjelović, O. (2024) · BioMedInformatics · doi:10.3390/biomedinformatics4010028 · Cited by 14
Liu, X., Wang, R., Lai, Y., Wu, Y., Cheng, H., Lu, Y., Zhang, J., Hao, N., Ban, C., Wang, Y., et al. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3381047 · Cited by 14
Benchmarking Pathology Feature Extractors for Whole Slide Image Classification
Wölflein, G., Ferber, D., Meneghetti, A. R., Nahhas, O. S. M. E., Truhn, D., Carrero, Z. I., Harrison, D. J., Arandjelović, O., & Kather, J. N. (2023) · St Andrews Research Repository (St Andrews Research Repository) · doi:10.48550/arxiv.2311.11772 · Cited by 13
Zhao, W., Luo, X., Tong, F., Zheng, X., Li, J., Zhao, G., & Zhao, D. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.11.041 · Cited by 13
Parvaiz, A., Nasir, E. S., & Fraz, M. M. (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-024-01049-2 · Cited by 12
The Tipping Point for Deep Learning in Oncology
Trister, A. D. (2019) · JAMA Oncology · doi:10.1001/jamaoncol.2019.1799 · Cited by 11
Multicenter, Multivendor Validation of an FDA-approved Algorithm for Mammography Triage
Retson, T., Watanabe, A. T., Vu, H., & Chim, C. Y. (2022) · Journal of Breast Imaging · doi:10.1093/jbi/wbac046 · Cited by 11
Manuel, C. T., Zehnder, P., Kaya, S., Sullivan, R., & Hu, F. (2022) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2022.100148 · Cited by 11
Brodsky, V., Levine, L., Solans, E. P., Dola, S., Chervony, L., & Polak, S. (2022) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2021-0550-oa · Cited by 11
Self-citationAbraham, T. M., Shaw, A., O'Connor, D., Todd, A. W., & Levenson, R. M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.08579 · Cited by 11
Promise and Challenges of Generative AI in Healthcare Information Systems
Ustymenko, S., & Phadke, A. (2024) · ACM Southeast Regional Conference (ACMSE) · doi:10.1145/3603287.3651196 · Cited by 10
Large Language Model in Financial Regulatory Interpretation
Cao, Z., & Feinstein, Z. (2024) · IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr) · doi:10.1109/cifer62890.2024.10772991 · Cited by 10
A video transformer network for thyroid cancer detection on hyperspectral histologic images
Tran, M. H., Gomez, O., & Fei, B. (2023) · SPIE Medical Imaging: Digital and Computational Pathology · doi:10.1117/12.2654851 · Cited by 10
A low-cost pathological image digitalization method based on 5 times magnification scanning
Sun, K. W., Gao, Y., Xie, T., Wang, X., Yang, Q., Chen, L., Wang, K., & Yu, G. (2022) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-21-749 · Cited by 9
Exploring Hyperspectral Histopathology Image Segmentation from a Deformable Perspective
Xie, X., Jin, T., Yun, B., Li, Q., & Wang, Y. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3611796 · Cited by 9
A novel transformer-based aggregation model for predicting gene mutations in lung adenocarcinoma
Sun, K., Zheng, Y., Yang, X., & Jia, W. (2024) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-023-03004-9 · Cited by 9
Learning a low dimensional manifold of real cancer tissue with PathologyGANPreprint
Quiros, A. C., Murray‐Smith, R., & Yuan, K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.06517 · Cited by 9
Federated Learning for Digital Pathology: A Pilot Study
Babu, G. M., Wong, K. W., & Parry, J. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.09.129 · Cited by 8
Navigating the path to precision: ChatGPT as a tool in pathology
Vaidyanathaiyer, R., Thanigaimani, G. D., Arumugam, P., Einstien, D., Ganesan, S., & Surapaneni, K. M. (2024) · Pathology - Research and Practice · doi:10.1016/j.prp.2024.155141 · Cited by 8
Jordan, D. M., Vy, H. M. T., & Do, R. (2023) · medRxiv · doi:10.1101/2023.12.21.23300393 · Cited by 7
Rodrigues, D., Reinhard, S., Waldburger, T., Martin, D. F., Couto, S. S., Zlobec, I., Caie, P. D., & Burlingame, E. A. (2023) · Cancer Research · doi:10.1158/1538-7445.am2023-5442 · Cited by 7
Yang, S., Pérez-Bueno, F., Castro-Macías, F. M., Molina, R., & Katsaggelos, A. K. (2023) · Digital Signal Processing · doi:10.1016/j.dsp.2023.104318 · Cited by 7
Improving Mitosis Detection on Histopathology Images Using Large Vision-Language Models
Ding, R., Hall, J., Tenenholtz, N., & Severson, K. (2024) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi56570.2024.10635613 · Cited by 7
Singh, V. K., Sarker, M. M. K., Makhlouf, Y., Craig, S. G., Humphries, M. P., Loughrey, M. B., James, J. A., Salto‐Tellez, M., O’Reilly, P. G., & Maxwell, P. (2022) · Cancers · doi:10.3390/cancers14163910 · Cited by 6
Hatta, S., Ichiuji, Y., Mabu, S., Kugler, M., Hontani, H., Okoshi, T., Fuse, H., Kawada, T., Kido, S., Imamura, Y., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-46472-7 · Cited by 5
Artifact Augmentation for Enhanced Tissue Detection in Microscope Scanner Systems
Küttel, D., Kovács, L., Szölgyén, Á., Paulik, R., Jónás, V. Z., Kozlovszky, M., & Molnar, B. A. (2023) · Sensors · doi:10.3390/s23229243 · Cited by 5
SetQuence & SetOmic: Deep set transformers for whole genome and exome tumour analysis
Jurenaite, N., León-Periñán, D., Donath, V., Torge, S., & Jäkel, R. (2023) · Biosystems · doi:10.1016/j.biosystems.2023.105095 · Cited by 5
Chen, Y., Zhao, W., & Yu, L. (2023) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi53787.2023.10230804 · Cited by 5
StainDiffuser: MultiTask Dual Diffusion Model for Virtual StainingPreprint
Kataria, T., Knudsen, B. S., & Elhabian, S. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2403.11340 · Cited by 4
Training and testing a CNN-based engine for brain MRI scan classification and segmentationPreprint
Dhawan, K., & Agrawal, U. (2023) · medRxiv · doi:10.1101/2023.07.03.23292122 · Cited by 3
Large language model’s utility in helping pathology professionals
Daungsupawong, H., & Wiwanitkit, V. (2023) · American Journal of Clinical Pathology · doi:10.1093/ajcp/aqad130 · Cited by 3
Automatic Report Generation for Histopathology images using pre-trained Vision TransformersPreprint
Sengupta, S., & Brown, D. E. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2311.06176 · Cited by 3
Training and Testing a CNN-based Engine for Brain MRI Scan Classification and Segmentation
Dhawan, K., & Agrawal, U. (2024) · International Journal of Science and Research (IJSR) · doi:10.21275/sr24102111504 · Cited by 3
HoechstGAN: Virtual Lymphocyte Staining Using Generative Adversarial NetworksPreprint
Wölflein, G., Um, I. H., Harrison, D. J., et al. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2210.06909 · Cited by 2
Artifact-Robust Graph-Based Learning in Digital Pathology
Gheshlaghi, S. H., Aryal, M., Yahyasoltani, N., & Ganji, M. (2026) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2026.3685637 · Cited by 2
Chandrasekhar, V. (2023) · International Conference on Information Communication and Signal Processing (ICICSP) · doi:10.1109/icicsp59554.2023.10390669 · 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
Ink removal in whole slide images using hallucinated data
Ramanathan, V., Han, W., Bassiouny, D., Rakovitch, E., & Martel, A. L. (2023) · SPIE Medical Imaging: Digital and Computational Pathology · doi:10.1117/12.2653281 · Cited by 2
Kocbek, P., Gosak, L., Musović, K., & Štiglic, G. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-09342-5_37 · Cited by 2
Diffusion-Enhanced Magnified Histopathological Images for Robust Breast Cancer Classification
Chengxiao, Y., Zeng, X., Ahmed, A., Tunio, M. H., & Fan, S. (2023) · International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) · doi:10.1109/iccwamtip60502.2023.10387068 · Cited by 2
clinical governance: two years experience of reporting discrepancy review in radiology
Vohrah, A., & Chandy, J. (2003) · Journal of Diagnostic Radiography and Imaging · doi:10.1017/s1460472803000051 · Cited by 1
Morel, L.-O., Dérangère, V., Arnould, L., Ladoire, S., & Vinçon, N. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.01.30.475484
Research works printed but not linked: 30
- Sharpiro, 2006, The Yale Book of Quotations
- Kumar, 2014, Professional Edition e-Book
- Bychkov, 2023, Constant demand, patchy supply, Pathologist, 18
- Jo, 2023, The promise and peril of generative AI, Nature, 614, 214
- Goodfellow IJ, Pouget-Abadie J, Mirza M, et al. Generative Adversarial Nets. Proceedings of the 27th International Conference on Neural Information Processing Systems. 2014;2:2672–2680.
- Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017;30:5998–6008.
- Hu, 2023, ChatGPT sets record for fastest-growing user base—analyst note, Reuters. February, 2
- Vaid, 2024, Generative Large Language Models are autonomous practitioners of evidence-based medicine. Preprint
- Hanif, 2024, International Conference on Medical Image Computing and Computer-Assisted Intervention, 443
- McCleary K, Ghawaly J, Miele L. TNM tumor classification from unstructured breast cancer pathology reports using LoRA finetuning of Mistral 7B. AAAI 2024 Spring Symposium on Clinical Foundation Models. May 3, 2024. https://openreview.net/pdf?id=rLVQmYYgJP. Accessed September 2, 2024.
- Abraham TM, Levenson R. A comparison of diffusion models and CycleGANs for virtual staining of slide-free microscopy images. ICML 2023 Workshop Deployable Generative AI. July 10, 2023. https://openreview.net/pdf?id=UhJAUTS8Ep. Accessed September 2, 2024.
- Hou, 2019, Robust histopathology image analysis: to label or to synthesize? Proceedings of the, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 8533
- Fernandez, 2022, International Workshop on Simulation and Synthesis in Medical Imaging, 79
- Oh, 2023, International Conference on Medical Image Computing and Computer-Assisted Intervention, 337
- Zhang, 2024, BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs, Preprint. Published online
- Tsakiroglou, 2024, Lymphoma triage from H&E using AI for improved clinical management, J Clin Pathol, 78, 28
- Lv, 2022, Joint Region-Attention and Multi-Scale Transformer for Microsatellite Instability Detection From Whole Slide Images in Gastrointestinal Cancer, 293
- Tran, 2023, B-Cos Aligned Transformers Learn Human-Interpretable Features, 514
- Zou Y. Predictive modeling of post radiation-therapy recurrence for gynecological cancer patients using clinical and histopathology imaging features [master’s thesis]. Montreal, Canada: McGill University; 2023. https://escholarship.mcgill.ca/concern/theses/mc87pw638. Published online 2023. Accessed December 7, 2024.
- Shokrollahi Y. Deep learning and generative AI approaches for automated diagnosis and personalized treatment: bridging machine learning, medicine, and biomechanics in predicting tissue mechanics and biomaterial properties [doctoral dissertation]. Melbourne, FL: Florida Institute of Technology; 2023. https://repository.fit.edu/cgi/viewcontent.cgi?article=2386&&context=etd&&sei-redir=1. Accessed December 7, 2024.
- Laohawetwanit, Accuracy of GPT-4 in histopathological image detection and classification of colorectal adenomas, J Clin Pathol
- Wu, 2023, Can GPT-4V (ision) serve medical applications: case studies on GPT-4V for multimodal medical diagnosis. Preprint
- Chen, 2024, MI-Gen: multiple instance generation of pathology reports for gigapixel whole-slide images. Preprint
- Laohawetwanit, 2024, A survey of the adoption of large language models by pathologists, Am J Clin Pathol
- Kim, 2023, International Conference on Medical Image Computing and Computer-Assisted Intervention, 245
- Liu, 2021, Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 479
- Qiu, 2023, International Conference on Medical Image Computing and Computer-Assisted Intervention, 90
- Meincke, 2024, Using large language models for idea generation in innovation, The Wharton School Research Paper
- Lee, 2024, Do large language models understand medical codes? Preprint
- Ma, 2024, Are large language models good prompt optimizers? Preprint
Cited by
Sorted by citations
A multimodal whole-slide foundation model for pathology
Ding, T., Wagner, S. J., Song, A. H., Chen, R. J., Lu, M. Y., Zhang, A., Vaidya, A. J., Jaume, G., Shaban, M., Kim, A., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03982-3 · Cited by 171
Self-citationThe 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
Chong, Y., & Bychkov, A. (2025) · Cytopathology · doi:10.1111/cyt.70023 · Cited by 12
Self-citationEvaluation of general-purpose large language models as diagnostic support tools in cervical cytology
Laohawetwanit, T., Apornvirat, S., Asaturova, A. V., Li, H., Lami, K., & Bychkov, A. (2025) · Pathology - Research and Practice · doi:10.1016/j.prp.2025.156159 · Cited by 9
Self-citationZannoni, G. F., Angelico, G., d’Amati, A., D’Alessandris, N., Scaglione, G., Urtueta, B. P., Ferrara, G., Caliò, A., Campisi, P., Leo, A. D., et al. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26167687 · Cited by 9
Metadata
- Authors
- Brodsky, V., Ullah, E., Bychkov, A., Song, A. H., Walk, E. E., Louis, P., Rasool, G., Singh, R. S., Mahmood, F., Bui, M. M., & Parwani, A. V.
- Affiliations
- Washington University in St. Louis (US)
- Counties Manukau District Health Board (NZ)
- Kameda Medical Center (JP)
- Brigham and Women's Hospital (US)
- PathAI (United States) (US)
- Rutgers, The State University of New Jersey (US)
- University of South Florida (US)
- Summit Medical Group (United States) (US)
- Moffitt Cancer Center (US)
- The Ohio State University (US)
- ORCID
- Venue
- Archives of Pathology & Laboratory Medicine
- Published
- 2025-01-21
- Volume
- 149
- Issue
- 4
- Pages
- 298-318
- ISSN
- 0003-9985 (ISSN-L)
- 1543-2165
- OpenAlex
- W4406666355
- Semantic Scholar
- 17e34c392460c419d75d13d1055c9dbd5b60733e
- Cited by
- 48
- References
- 234
- Citations by year
- 2026: 27 · 2025: 21
- FWCI
- 2.90
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Archives of Pathology & Laboratory Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- generative AI, pathological anatomy, virtual staining, workflow efficiency, reflex testing, AI-generated educational content, synthetic data generation, federated learning, prompt engineering, AI validation, ethical considerations, clinical integration
- MeSH terms
- Generative Artificial Intelligence, Artificial Intelligence, Humans, Pathology, Pathology, Clinical, Workflow
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