Generative Artificial Intelligence in Anatomic Pathology

Highly accurate protein structure prediction with AlphaFold

Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764

Accurate structure prediction of biomolecular interactions with AlphaFold 3

Abramson, J., Adler, J., Dunger, J., Evans, R., Green, T., Pritzel, A., Ronneberger, O., Willmore, L., Ballard, A. J., Bambrick, J., et al. (2024) · Nature · doi:10.1038/s41586-024-07487-w · Cited by 15,861

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

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

Cancer statistics, 2024

Siegel, R. L., Giaquinto, A. N., & Jemal, A. (2024) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21820 · Cited by 9,533

Evolutionary-scale prediction of atomic level protein structure with a language model

Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., et al. (2022) · doi:10.1101/2022.07.20.500902 · Cited by 5,990

Low Health Literacy and Health Outcomes: An Updated Systematic Review

Berkman, N. D., Sheridan, S. L., Donahue, K. E., Halpern, D. J., & Crotty, K. (2011) · Annals of Internal Medicine · doi:10.7326/0003-4819-155-2-201107190-00005 · Cited by 5,505

Deep learning in medical image analysis

Shen, D., Wu, G., & Suk, H.-I. (2017) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-071516-044442 · Cited by 4,968

Chain-Of-Thought Prompting Elicits Reasoning in Large Language Models

Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q. V., & Zhou, D. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-1800 · Cited by 4,505

Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models

Kung, T. H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., et al. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000198 · Cited by 4,115

Diffusion Models Beat GANs on Image Synthesis

Dhariwal, P., & Nichol, A. (2021) · arXiv · doi:10.48550/arxiv.2105.05233 · Cited by 3,362

Accurate proteome-wide missense variant effect prediction with AlphaMissense

Cheng, J., Novati, G., Pan, J., Bycroft, C., Žemgulytė, A., Applebaum, T., Pritzel, A., Wong, L. H., Zielinski, M., Sargeant, T., et al. (2023) · Science · doi:10.1126/science.adg7492 · Cited by 2,163

How Does ChatGPT Perform on the United States Medical Licensing Examination (USMLE)? The Implications of Large Language Models for Medical Education and Knowledge Assessment

Gilson, A., Safranek, C. W., Huang, T., Socrates, V., Chi, L., Taylor, R. A., & Chartash, D. (2023) · JMIR Medical Education · doi:10.2196/45312 · Cited by 2,078

Benefits, Limits, and Risks of GPT-4 as an AI Chatbot for Medicine

Lee, P., Bubeck, S., & Petro, J. (2023) · New England Journal of Medicine · doi:10.1056/nejmsr2214184 · Cited by 1,738

scGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI

Cui, H., Wang, C., Maan, H., Pang, K., Luo, F., & Wang, B. (2023) · doi:10.1101/2023.04.30.538439 · Cited by 1,694

Tumor heterogeneity: Causes and consequences

Marusyk, A., & Polyák, K. (2009) · Biochimica et Biophysica Acta (BBA) - Reviews on Cancer · doi:10.1016/j.bbcan.2009.11.002 · Cited by 1,646

Towards a general-purpose foundation model for computational pathology

Chen, R. J., Ding, T., Lu, M. Y., Williamson, D. F. K., Jaume, G., Song, A. H., Chen, B., Zhang, A., Shao, D., Shaban, M., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02857-3 · Cited by 1,560

CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows

Dong, X., Bao, J., Chen, D., Zhang, W., Yu, N., Yuan, L., Chen, D., & Guo, B. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01181 · Cited by 1,533

Building Watson: An Overview of the DeepQA Project

Ferrucci, D. A., Brown, E. W., Chu‐Carroll, J., Fan, J., Gondek, D. C., Kalyanpur, A. A., Lally, A., Murdock, J. W., Nyberg, E., Prager, J. M., et al. (2010) · AI Magazine · doi:10.1609/aimag.v31i3.2303 · Cited by 1,523

Learning From Crowds

Raykar, V. C., Yu, S., Zhao, L. H., Valadez, G. H., Florin, C., Bogoni, L., & Moy, L. P. (2010) · Journal of Machine Learning Research · doi:10.5555/1756006.1859894 · Cited by 1,209

Large language models generate functional protein sequences across diverse families

Madani, A., Krause, B., Greene, E. R., Subramanian, S., Mohr, B. P., Holton, J. M., Olmos, J. L., Xiong, C., Sun, Z. Z., Socher, R., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-022-01618-2 · Cited by 1,078

The imperative for regulatory oversight of large language models (or generative AI) in healthcare

Meskó, B., & Topol, E. J. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00873-0 · Cited by 1,047

A visual-language foundation model for computational pathology

Lu, M. Y., Chen, B., Williamson, D. F. K., Chen, R. J., Liang, I., Ding, T., Jaume, G., Odintsov, I., Le, L. P., Gerber, G., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02856-4 · Cited by 966

A whole-slide foundation model for digital pathology from real-world data

Xu, H., Usuyama, N., Bagga, J., Zhang, S., Rao, R., Naumann, T., Wong, C., Gero, Z., González, J., Gu, Y., et al. (2024) · Nature · doi:10.1038/s41586-024-07441-w · Cited by 921

Disease variant prediction with deep generative models of evolutionary data

Frazer, J., Notin, P., Dias, M., Gomez, A., Min, J. K., Brock, K., Gal, Y., & Marks, D. S. (2021) · Nature · doi:10.1038/s41586-021-04043-8 · Cited by 791

Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks

Hollon, T. C., Pandian, B., Adapa, A. R., Urias, E., Save, A. V., Khalsa, S. S. S., Eichberg, D. G., D’Amico, R. S., Farooq, Z. U., Lewis, S., et al. (2020) · Nature Medicine · doi:10.1038/s41591-019-0715-9 · Cited by 790

Transformer for Single Image Super-Resolution

Lu, Z., Li, J., Liu, H., Huang, C., Zhang, L., & Zeng, T. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00061 · Cited by 755

Algorithmic fairness in artificial intelligence for medicine and healthcare

Chen, R. J., Wang, J. J., Williamson, D. F. K., Chen, T. Y., Lipkova, J., Lu, M. Y., Sahai, S., & Mahmood, F. (2023) · Nature Biomedical Engineering · doi:10.1038/s41551-023-01056-8 · Cited by 750

Synthetic data in machine learning for medicine and healthcare

Chen, R. J., Lu, M. Y., Chen, T. Y., Williamson, D. F. K., & Mahmood, F. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-021-00751-8 · Cited by 728

Large Language Model Influence on Diagnostic Reasoning: A Randomized Clinical Trial

Goh, E., Gallo, R., Hom, J., Strong, E., Weng, Y., Kerman, H., Cool, J. A., Kanjee, Z., Parsons, A. S., Ahuja, N., et al. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.40969 · Cited by 724

Teacher support and student motivation to learn with Artificial Intelligence (AI) based chatbot

Chiu, T. K. F., Moorhouse, B. L., Chai, C. S., & Ismailov, M. (2024) · Interactive Learning Environments · doi:10.1080/10494820.2023.2172044 · Cited by 707

Beyond Self-Attention: External Attention Using Two Linear Layers for Visual Tasks

Guo, M.-H., Liu, Z.-N., Mu, T., & Hu, S. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3211006 · Cited by 703

A visual–language foundation model for pathology image analysis using medical twitter

Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T. J., & Zou, J. (2023) · Nature Medicine · doi:10.1038/s41591-023-02504-3 · Cited by 693

Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks

Roa, Á. A. C., Basavanhally, A. N., Gonzalez, F. A., Gilmore, H. L., Feldman, M. D., Ganesan, S., Shih, N. N. C., Tomaszewski, J., & Madabhushi, A. (2014) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2043872 · Cited by 621

Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI): a clinical safety analysis of a randomised, controlled, non-inferiority, single-blinded, screening accuracy study

Lång, K., Josefsson, V., Larsson, A.-M., Larsson, S., Högberg, C., Sartor, H., Hofvind, S., Andersson, I., & Rosso, A. (2023) · The Lancet Oncology · doi:10.1016/s1470-2045(23)00298-x · Cited by 598

Proceedings of the 31st ACM International Conference on Multimedia

(2023) · doi:10.1145/3581783 · Cited by 592

A pathology foundation model for cancer diagnosis and prognosis prediction

Wang, X., Zhao, J., Marostica, E., Yuan, W., Jin, J., Zhang, J., Li, R., Tang, H., Wang, K., Li, Y., et al. (2024) · Nature · doi:10.1038/s41586-024-07894-z · Cited by 580

ChatGPT makes medicine easy to swallow: an exploratory case study on simplified radiology reports

Jeblick, K., Schachtner, B., Dexl, J., Mittermeier, A., Stüber, A. T., Topalis, J., Weber, T., Wesp, P., Sabel, B. O., Ricke, J., et al. (2023) · European Radiology · doi:10.1007/s00330-023-10213-1 · Cited by 549

Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models

Zhang, Y., Li, Y., Cui, L., Cai, D., Liu, L., Fu, T., Huang, X., Zhao, E., Zhang, Y., Chen, Y., et al. (2025) · Computational Linguistics · doi:10.1162/coli.a.16 · Cited by 534

LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Jiang, D., Ren, X., & Lin, B. Y. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.792 · Cited by 506

On the conversational persuasiveness of GPT-4

Salvi, F., Ribeiro, M. H., Gallotti, R., & West, R. (2025) · Nature Human Behaviour · doi:10.1038/s41562-025-02194-6 · Cited by 480

Shifting machine learning for healthcare from development to deployment and from models to data

Zhang, A., Xing, L., Zou, J., & Wu, J. C. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00898-y · Cited by 420

Light-sheet microscopy for slide-free non-destructive pathology of large clinical specimens

Glaser, A. K., Reder, N. P., Chen, Y., McCarty, E. F., Yin, C., Wei, L., Wang, Y., True, L. D., & Liu, J. T. C. (2017) · Nature Biomedical Engineering · doi:10.1038/s41551-017-0084 · Cited by 419

Benchmarking Large Language Models for News Summarization

Zhang, T., Ladhak, F., Durmus, E., Liang, P., McKeown, K., & Hashimoto, T. B. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00632 · Cited by 415

Generative AI in Medicine and Healthcare: Promises, Opportunities and Challenges

Zhang, P., & Kamel Boulos, M. N. (2023) · Future Internet · doi:10.3390/fi15090286 · Cited by 411

CellViT: Vision Transformers for precise cell segmentation and classification

Hörst, F., Rempe, M., Heine, L., Seibold, C., Keyl, J., Baldini, G., Ugurel, S., Siveke, J. T., Grünwald, B. T., Egger, J., et al. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103143 · Cited by 401

Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide Images

Chen, R. J., Lu, M. Y., Weng, W.-H., Chen, T. Y., Williamson, D. F., Manz, T., Shady, M., & Mahmood, F. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00398 · Cited by 398

Using AI-generated suggestions from ChatGPT to optimize clinical decision support

Liu, S., Wright, A. P., Patterson, B. L., Wanderer, J. P., Turer, R. W., Nelson, S. D., McCoy, A. B., Sittig, D. F., & Wright, A. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad072 · Cited by 397

Deep learning-enabled virtual histological staining of biological samples

Bai, B., Yang, X., Li, Y., Zhang, Y., Pillar, N., & Özcan, A. (2023) · Light Science & Applications · doi:10.1038/s41377-023-01104-7 · Cited by 370

Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images

Mahmood, F., Borders, D., Chen, R. J., McKay, G. N., Salimian, K. J., Baras, A. S., & Durr, N. J. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2927182 · Cited by 361

HistoQC: An Open-Source Quality Control Tool for Digital Pathology Slides

Janowczyk, A. R., Zuo, R., Gilmore, H. L., Feldman, M. D., & Madabhushi, A. (2019) · JCO Clinical Cancer Informatics · doi:10.1200/cci.18.00157 · Cited by 357

Artificial intelligence for digital and computational pathology

Song, A. H., Jaume, G., Williamson, D. F. K., Lu, M. Y., Vaidya, A., Miller, T. R., & Mahmood, F. (2023) · Nature Reviews Bioengineering · doi:10.1038/s44222-023-00096-8 · Cited by 356

The impact of site-specific digital histology signatures on deep learning model accuracy and bias

Howard, F. M., Dolezal, J., Kochanny, S., Schulte, J., Chen, H., Heij, L., Huo, D., Nanda, R., Olopade, O. I., Kather, J. N., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-24698-1 · Cited by 330

A multimodal comparison of latent denoising diffusion probabilistic models and generative adversarial networks for medical image synthesis

Müller‐Franzes, G. A., Niehues, J. M., Khader, F., Arasteh, S. T., Haarburger, C., Kuhl, C. K., Wang, T., Han, T., Nolte, T., Nebelung, S., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-39278-0 · Cited by 324

Towards artificial general intelligence via a multimodal foundation model

Fei, N., Lu, Z., Gao, Y., Yang, G., Huo, Y., Wen, J., Lu, H., Song, R., Gao, X., Xiang, T., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30761-2 · Cited by 318

Leveraging large language models for decision support in personalized oncology

Benary, M., Wang, X. D., Schmidt, M., Soll, D., Hilfenhaus, G., Nassir, M., Sigler, C., Knödler, M., Keller, U., Beule, D., et al. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.43689 · Cited by 306

Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Gemini Team, Georgiev, P., Lei, V. I., Burnell, R., Bai, L., Gulati, A., Tanzer, G., Vincent, D., Pan, Z., Wang, S., et al. (2024) · arXiv · doi:10.48550/arxiv.2403.05530 · Cited by 306

GenAI against humanity: nefarious applications of generative artificial intelligence and large language models

Ferrara, E. (2024) · Journal of Computational Social Science · doi:10.1007/s42001-024-00250-1 · Cited by 296

On the Conversational Persuasiveness of Large Language Models: A Randomized Controlled Trial

Salvi, F., Ribeiro, M. H., Gallotti, R., & West, R. (2024) · doi:10.21203/rs.3.rs-4429707/v1 · Cited by 285

Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study

Wagner, S. J., Reisenbüchler, D., West, N. P., Niehues, J. M., Zhu, J., Foersch, S., Veldhuizen, G. P., Quirke, P., Grabsch, H. I., van den Brandt, P. A., et al. (2023) · Cancer Cell · doi:10.1016/j.ccell.2023.08.002 · Cited by 275

Microscopy with ultraviolet surface excitation for rapid slide-free histology

Fereidouni, F., Harmany, Z. T., Tian, M., Todd, A. W., Kintner, J. A., McPherson, J. D., Borowsky, A. D., Bishop, J. W., Lechpammer, M., Demos, S. G., et al. (2017) · Nature Biomedical Engineering · doi:10.1038/s41551-017-0165-y · Cited by 267

ChatGPT is not all you need. A state-of-the-art review of large generative AI models

Gozalo-Brizuela, R., & Garrido-Merchan, E. C. (2023) · arXiv · doi:10.48550/arxiv.2301.04655 · Cited by 245

An exploratory survey about using ChatGPT in education, healthcare, and research

Hosseini, M., Gao, C. A., Liebovitz, D. M., Carvalho, A. M., Ahmad, F. S., Luo, Y., MacDonald, N., Holmes, K. L., & Kho, A. (2023) · PLOS ONE · doi:10.1371/journal.pone.0292216 · Cited by 238

Design of highly functional genome editors by modelling CRISPR–Cas sequences

Ruffolo, J. A., Nayfach, S., Gallagher, J. P., Bhatnagar, A., Beazer, J., Hussain, R., Russ, J., Yip, J., Hill, E., Pačesa, M., et al. (2025) · Nature · doi:10.1038/s41586-025-09298-z · Cited by 233

Large language model (ChatGPT) as a support tool for breast tumor board

Sorin, V., Klang, E., Sklair‐Levy, M., Cohen, I., Zippel, D. B., Lahat, N. B., Konen, E., & Barash, Y. (2023) · npj Breast Cancer · doi:10.1038/s41523-023-00557-8 · Cited by 223

Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology

Zhang, D., Schroeder, A., Yan, H., Yang, H., Hu, J., Lee, M. Y. Y., Cho, K. S., Susztak, K., Xu, G. X., Feldman, M. D., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-02019-9 · Cited by 219

Will Generative Artificial Intelligence Deliver on Its Promise in Health Care?

Wachter, R. M., & Brynjolfsson, E. (2023) · JAMA · doi:10.1001/jama.2023.25054 · Cited by 206

Automated paper screening for clinical reviews using large language models: Data analysis study

Guo, E., Gupta, M., Deng, J., Park, Y.-J., Paget, M., & Naugler, C. (2024) · Journal of Medical Internet Research · doi:10.2196/48996 · Cited by 205

Large language models surpass human experts in predicting neuroscience results

Luo, X., Rechardt, A., Sun, G., Nejad, K. K., Yáñez, F., Yilmaz, B., Lee, K., Cohen, A. O., Borghesani, V., Pashkov, A. A., et al. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-02046-9 · Cited by 204

Deep learning in generating radiology reports: A survey

Monshi, M. M. A., Poon, J., & Chung, Y. Y. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101878 · Cited by 201

Factuality challenges in the era of large language models and opportunities for fact-checking

Augenstein, I., Baldwin, T., Cha, M., Chakraborty, T., Ciampaglia, G. L., Corney, D., DiResta, R., Ferrara, E., Hale, S., Halevy, A., et al. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00881-z · Cited by 187

Modeling Dense Multimodal Interactions Between Biological Pathways and Histology for Survival Prediction

Jaume, G., Vaidya, A. J., Chen, R. J., Williamson, D. F. K., Liang, P. P., & Mahmood, F. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01100 · Cited by 184

Precision histology: how deep learning is poised to revitalize histomorphology for personalized cancer care

Djuric, U., Zadeh, G. M., Aldape, K., & Diamandis, P. (2017) · npj Precision Oncology · doi:10.1038/s41698-017-0022-1 · Cited by 179

Whole Slide Imaging: Technology and Applications

Hanna, M. G., Parwani, A. V., & Sirintrapun, S. J. (2020) · Advances in Anatomic Pathology · doi:10.1097/pap.0000000000000273 · Cited by 171

LIDA: A Tool for Automatic Generation of Grammar-Agnostic Visualizations and Infographics using Large Language Models

Dibia, V. C. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-demo.11 · Cited by 170

Implementation of Digital Pathology Offers Clinical and Operational Increase in Efficiency and Cost Savings

Hanna, M. G., Reuter, V. E., Samboy, J., England, C., Corsale, L., Fine, S. W., Agaram, N. P., Stamelos, E., Yagi, Y., Hameed, M., et al. (2019) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2018-0514-oa · Cited by 168

The originality of machines: AI takes the Torrance test

Guzik, E. E., Byrge, C., & Gilde, C. (2023) · Journal of Creativity · doi:10.1016/j.yjoc.2023.100065 · Cited by 165

Generative models improve fairness of medical classifiers under distribution shifts

Ktena, I., Wiles, O., Albuquerque, I., Rebuffi, S.-A., Tanno, R., Roy, A. G., Azizi, S., Belgrave, D., Kohli, P., Cemgil, T., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02838-6 · Cited by 165

Unpaired Stain Transfer Using Pathology-Consistent Constrained Generative Adversarial Networks

Liu, S., Zhang, B., Liu, Y., Han, A., Shi, H., Guan, T., & He, Y. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3069874 · Cited by 163

Revolutionizing Digital Pathology With the Power of Generative Artificial Intelligence and Foundation Models

Waqas, A., Bui, M. M., Glassy, E. F., Naqa, I. E., Borkowski, P., Borkowski, A., & Rasool, G. (2023) · Laboratory Investigation · doi:10.1016/j.labinv.2023.100255 · Cited by 158

Selective synthetic augmentation with HistoGAN for improved histopathology image classification

Xue, Y., Ye, J., Zhou, Q., Long, L. R., Antani, S., Xue, Z., Cornwell, C., Zaino, R. J., Cheng, K. C., & Huang, X. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101816 · Cited by 153

Deep learning-inferred multiplex immunofluorescence for immunohistochemical image quantification

Ghahremani, P., Li, Y., Kaufman, A. E., Vanguri, R., Greenwald, N. F., Angelo, M., Hollmann, T. J., & Nadeem, S. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00471-x · Cited by 152

Stain normalization of histopathology images using generative adversarial networks

Zanjani, F. G., Zinger, S., Bejnordi, B. E., van der Laak, J. A. W. M., & de With, P. H. N. (2018) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2018.8363641 · Cited by 148

Efficient healthcare with large language models: optimizing clinical workflow and enhancing patient care

Tripathi, S., Sukumaran, R., & Cook, T. S. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad258 · Cited by 147

LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Jiang, D., Ren, X., & Lin, B. Y. (2023) · arXiv · doi:10.48550/arxiv.2306.02561 · Cited by 141

Adversarial attacks and adversarial robustness in computational pathology

Laleh, N. G., Truhn, D., Veldhuizen, G. P., Han, T., van Treeck, M., Buelow, R. D., Langer, R., Dislich, B., Boor, P., Schulz, V., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-33266-0 · Cited by 137

Artificial intelligence hallucinations

Salvagno, M., Taccone, F. S., & Gerli, A. G. (2023) · Critical Care · doi:10.1186/s13054-023-04473-y · Cited by 136

A Survey of Hallucination in Large Foundation Models

Rawte, V., Sheth, A., & Das, A. (2023) · arXiv · doi:10.48550/arxiv.2309.05922 · Cited by 133

A promising deep learning-assistive algorithm for histopathological screening of colorectal cancer

Ho, C., Zhao, Z., Chen, X. F., Sauer, J., Saraf, S. A., Jialdasani, R., Taghipour, K., Sathe, A. R., Khor, L.-Y., Lim, K., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-06264-x · Cited by 132

Human Cortical Activity Evoked by the Assignment of Authenticity when Viewing Works of Art

Huang, M., Bridge, H., Kemp, M., & Parker, A. J. (2011) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2011.00134 · Cited by 130

A Morphology Focused Diffusion Probabilistic Model for Synthesis of Histopathology Images

Moghadam, P. A., Dalen, S. V., Martin, K. C., Lennerz, J. K., Yip, S. S. F., Farahani, H., & Bashashati, A. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00204 · Cited by 130

Generative AI for Synthetic Data Generation: Methods, Challenges and the FuturePreprint

Guo, X., & Chen, Y. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2403.04190 · Cited by 130

A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models

Tonmoy, S. M. T. I., Zaman, S. M. M., Jain, V., Rani, A., Rawte, V., Chadha, A., & Das, A. (2024) · arXiv · doi:10.48550/arxiv.2401.01313 · Cited by 129

Synthesis of diagnostic quality cancer pathology images by generative adversarial networks

Levine, A., Peng, J., Farnell, D., Nursey, M., Wang, Y., Naso, J. R., Ren, H., Farahani, H., Chen, C., Chiu, D. S., et al. (2020) · The Journal of Pathology · doi:10.1002/path.5509 · Cited by 128

Foundation models for fast, label-free detection of glioma infiltration

Kondepudi, A., Pekmezci, M., Hou, X., Scotford, K., Jiang, C., Rao, A. T., Harake, E. S., Chowdury, A., Al‐Holou, W. N., Wang, L., et al. (2024) · Nature · doi:10.1038/s41586-024-08169-3 · Cited by 128

Triage-driven diagnosis of Barrett’s esophagus for early detection of esophageal adenocarcinoma using deep learning

Gehrung, M., Crispin‐Ortuzar, M., Berman, A. G., O’Donovan, M., Fitzgerald, R. C., & Markowetz, F. M. (2021) · Nature Medicine · doi:10.1038/s41591-021-01287-9 · Cited by 127

Extracting structured information from unstructured histopathology reports using generative pre‐trained transformer 4 ( GPT ‐4)

Truhn, D., Loeffler, C. M., Müller‐Franzes, G., Nebelung, S., Hewitt, K. J., Brandner, S., Bressem, K. K., Foersch, S., & Kather, J. N. (2024) · The Journal of Pathology · doi:10.1002/path.6232 · Cited by 124

Observer reliability in histological grading of astrocytoma stereotactic biopsies

Mittler, M. A., Walters, B. C., & Stopa, E. G. (1996) · Journal of neurosurgery · doi:10.3171/jns.1996.85.6.1091 · Cited by 121

DeepFocus: Detection of out-of-focus regions in whole slide digital images using deep learning

Şenaras, Ç., Niazi, M. K. K., Lozanski, G., & Gürcan, M. N. (2018) · PLoS ONE · doi:10.1371/journal.pone.0205387 · Cited by 119

Lumina-T2X: Transforming Text into Any Modality, Resolution, and Duration via Flow-based Large Diffusion TransformersPreprint

Gao, P., Zhuo, L., Liu, D., Du, R., Xu, L., Qiu, L., Zhang, Y., Lin, C., Huang, R., Geng, S., et al. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2405.05945 · Cited by 118

The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery

Lu, C., Lu, C., Lange, R. T., Foerster, J., Clune, J., & Ha, D. (2024) · arXiv · doi:10.48550/arxiv.2408.06292 · Cited by 118

Bias and Class Imbalance in Oncologic Data—Towards Inclusive and Transferrable AI in Large Scale Oncology Data Sets

Tasci, E., Zhuge, Y., Camphausen, K., & Krauze, A. V. (2022) · Cancers · doi:10.3390/cancers14122897 · Cited by 117

Capabilities of Gemini Models in Medicine

Saab, K., Tu, T., Weng, W.-H., Tanno, R., Stutz, D., Wulczyn, E., Zhang, F., Strother, T., Park, C., Vedadi, E., et al. (2024) · arXiv · doi:10.48550/arxiv.2404.18416 · Cited by 117

ChatGPT-4: An assessment of an upgraded artificial intelligence chatbot in the United States Medical Licensing Examination

Mihalache, A., Huang, R. S., Popovic, M. M., & Muni, R. H. (2023) · Medical Teacher · doi:10.1080/0142159x.2023.2249588 · Cited by 113

Expanding Cosmetic Plastic Surgery Research With ChatGPT

Gupta, R., Park, J. B., Bisht, C., Herzog, I., Weisberger, J., Chao, J., Chaiyasate, K., & Lee, E. S. (2023) · Aesthetic Surgery Journal · doi:10.1093/asj/sjad069 · Cited by 112

A Survey on Deep Learning and Explainability for Automatic Report Generation from Medical Images

Messina, P., Pino, P., Parra, D., Soto, Á., Besa, C., Uribe, S. A., Andía, M. E., Cristián Tejos, N., Prieto, C., & Daniel Capurro, N. (2022) · ACM Computing Surveys · doi:10.1145/3522747 · Cited by 111

Prostate Cancer Risk Stratification via Nondestructive 3D Pathology with Deep Learning–Assisted Gland Analysis

Xie, W., Reder, N. P., Koyuncu, C., Leo, P., Hawley, S. J., Huang, H., Mao, C., Postupna, N. O., Kang, S., Serafin, R., et al. (2021) · Cancer Research · doi:10.1158/0008-5472.can-21-2843 · Cited by 108

Generative Adversarial Networks in Digital Pathology and Histopathological Image Processing: A Review

Jose, L., Liu, S., Russo, C., Nadort, A., & Ieva, A. D. (2021) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_103_20 · Cited by 107

Attention-Challenging Multiple Instance Learning for Whole Slide Image Classification

Zhang, Y., Li, H., Sun, Y., Zheng, S., Zhu, C., & Yang, L. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73668-1_8 · Cited by 107

Accelerating histopathology workflows with generative AI-based virtually multiplexed tumour profiling

Pati, P., Karkampouna, S., Bonollo, F., Compérat, É., Radić, M., Spahn, M., Martinelli, A. L., Wartenberg, M., Julio, M. K., & Rapsomaniki, M. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00889-5 · Cited by 96

Design of highly functional genome editors by modeling the universe of CRISPR-Cas sequences

Ruffolo, J. A., Nayfach, S., Gallagher, J., Bhatnagar, A., Beazer, J., Hussain, R., Russ, J., Yip, J., Hill, E., Pacesa, M., et al. (2024) · doi:10.1101/2024.04.22.590591 · Cited by 90

Supporting Sensemaking of Large Language Model Outputs at Scale

Gero, K. I., Swoopes, C., Gu, Z., Kummerfeld, J. K., & Glassman, E. L. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642139 · Cited by 90

Virtual formalin-fixed and paraffin-embedded staining of fresh brain tissue via stimulated Raman CycleGAN model

Liu, Z., Chen, L., Cheng, H., Ao, J., Xiong, J., Liu, X., Chen, Y., Mao, Y. Q., & Ji, M. (2024) · Science Advances · doi:10.1126/sciadv.adn3426 · Cited by 88

A deep-learning model for transforming the style of tissue images from cryosectioned to formalin-fixed and paraffin-embedded

Ozyoruk, K. B., Can, S., Darbaz, B., Başak, K., Demir, D., Gokceler, G. I., Serin, G., Hacisalihoglu, U. P., Kurtuluş, E., Lu, M. Y., et al. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00952-9 · Cited by 86

Nuclei segmentation using attention aware and adversarial networks

Göçeri, E. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127445 · Cited by 81

Large language models and multimodal foundation models for precision oncology

Truhn, D., Eckardt, J.-N., Ferber, D., & Kather, J. N. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00573-2 · Cited by 79

The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models

Rozowsky, J. S., Gao, J., Borsari, B., Yang, Y., Galeev, T. R., Gürsoy, G., Epstein, C. B., Xiong, K., Xu, J., Li, T., et al. (2023) · Cell · doi:10.1016/j.cell.2023.02.018 · Cited by 79

Deep learning-enabled realistic virtual histology with ultraviolet photoacoustic remote sensing microscopy

Martell, M. T., Haven, N. J. M., Cikaluk, B. D., Restall, B. S., McAlister, E. A., Mittal, R., Adam, B., Giannakopoulos, N. V., Peiris, L. J., Silverman, S., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-41574-2 · Cited by 78

Deep learning generates synthetic cancer histology for explainability and education

Dolezal, J. M., Wolk, R., Hieromnimon, H. M., Howard, F. M., Srisuwananukorn, A., Karpeyev, D., Ramesh, S., Kochanny, S. E., Kwon, J. W., Agni, M., et al. (2023) · npj Precision Oncology · doi:10.1038/s41698-023-00399-4 · Cited by 75

Harnessing the Power of Generative AI for Clinical Summaries: Perspectives From Emergency Physicians

Barak-Corren, Y., Wolf, R., Rozenblum, R., Creedon, J. K., Lipsett, S. C., Lyons, T. W., Michelson, K. A., Miller, K. A., Shapiro, D. J., Reis, B. Y., et al. (2024) · Annals of Emergency Medicine · doi:10.1016/j.annemergmed.2024.01.039 · Cited by 75

Prevalence of Inter-Institutional Anatomic Pathology Slide Review

GUPTA, D. K. D., & Layfield, L. J. (2000) · The American Journal of Surgical Pathology · doi:10.1097/00000478-200002000-00014 · Cited by 74

Rapid discovery of high-affinity antibodies via massively parallel sequencing, ribosome display and affinity screening

Porebski, B. T., Balmforth, M. R., Browne, G. J., Riley, A., Jamali, K., Fürst, M. J. L. J., Velic, M., Buchanan, A. G., Minter, R., Vaughan, T. J., et al. (2023) · Nature Biomedical Engineering · doi:10.1038/s41551-023-01093-3 · Cited by 74

Artificial intelligence for breast cancer detection in mammography: experience of use of the ScreenPoint Medical Transpara system in 310 Japanese women

Sasaki, M., Tozaki, M., Rodríguez‐Ruiz, A., Yotsumoto, D., Ichiki, Y., Terawaki, A., Oosako, S., Sagara, Y., & Sagara, Y. (2020) · Breast Cancer · doi:10.1007/s12282-020-01061-8 · Cited by 67

Applications of discriminative and deep learning feature extraction methods for whole slide image analysis: A survey

Al-Thelaya, K., Gilal, N. U., Alzubaidi, M. S., Majeed, F., Agus, M., Schneider, J., & Househ, M. S. (2023) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2023.100335 · Cited by 67

Empirical Study of Overfitting in Deep Learning for Predicting Breast Cancer Metastasis

Xu, C., Coen-Pirani, P., & Jiang, X. (2023) · Cancers · doi:10.3390/cancers15071969 · Cited by 63

Innovations in surgical training: exploring the role of artificial intelligence and large language models (LLM)

Varas, J., Coronel, B. V., Villagrán, I., Escalona, G., HERNANDEZ, R., Schuit, G., Espinoza, V. D., Lagos, A., Jarry, C., Neyem, A., et al. (2023) · Revista do Colégio Brasileiro de Cirurgiões · doi:10.1590/0100-6991e-20233605-en · Cited by 63

Applications of artificial intelligence in clinical laboratory genomics

Aradhya, S., Facio, F. M., Metz, H. C., Manders, T. R., Colavin, A., Kobayashi, Y., Nykamp, K., Johnson, B. A., & Nussbaum, R. L. (2023) · American Journal of Medical Genetics Part C Seminars in Medical Genetics · doi:10.1002/ajmg.c.32057 · Cited by 62

Visualization Generation with Large Language Models: An EvaluationPreprint

Wang, X., Liang, C., Zheng, S., Liang, J., Li, G., Zhang, Y., & Liu, C. H. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2401.11255 · Cited by 60

Perspectives of Oncologists on the Ethical Implications of Using Artificial Intelligence for Cancer Care

Hantel, A., Walsh, T. P., Marron, J. M., Kehl, K. L., Sharp, R. R., Allen, E. M. V., & Abel, G. A. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.4077 · Cited by 58

PathSRGAN: Multi-Supervised Super-Resolution for Cytopathological Images Using Generative Adversarial Network

Ma, J., Yu, J., Liu, S., Chen, L., Li, X., Feng, J., Chen, Z., Zeng, S., Liu, X., & Cheng, S. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2980839 · Cited by 58

Assessing and understanding creativity in large language models

Zhao, Y., Zhang, R., Li, W., & Li, L. (2025) · Machine Intelligence Research · doi:10.1007/s11633-025-1546-4 · Cited by 57

Applications of Large Language Models in Pathology

Cheng, J. (2024) · Bioengineering · doi:10.3390/bioengineering11040342 · Cited by 56

Charting new AI education in gastroenterology: Cross-sectional evaluation of ChatGPT and perplexity AI in medical residency exam

Gravina, A. G., Pellegrino, R., Palladino, G., Imperio, G., Ventura, A., & Federico, A. (2024) · Digestive and Liver Disease · doi:10.1016/j.dld.2024.02.019 · Cited by 53

Recommendations for Performance Evaluation of Machine Learning in Pathology: A Concept Paper From the College of American Pathologists

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 52

Fake Publications in Biomedical Science: Red-flagging Method Indicates Mass Production

Sabel, B. A., Knaack, E., Gigerenzer, G., & Bilc, M. (2023) · doi:10.1101/2023.05.06.23289563 · Cited by 51

AI-MARRVEL — A Knowledge-Driven AI System for Diagnosing Mendelian Disorders

Mao, D., Liu, C., Wang, L., AI-Ouran, R., Deisseroth, C., Pasupuleti, S., Kim, S. Y., Li, L., Rosenfeld, J. A., Meng, L., et al. (2024) · NEJM AI · doi:10.1056/aioa2300009 · Cited by 51

Fake publications in biomedical science: red-flagging method indicates mass production

Sabel, B. A., Knaack, E., Gigerenzer, G., & Bilc, M.-I. (2026) · Naunyn-Schmiedeberg's Archives of Pharmacology · doi:10.1007/s00210-025-04275-9 · Cited by 51

A Foundational Multimodal Vision Language AI Assistant for Human PathologyPreprint

Lu, M. Y., Chen, B., Williamson, D. F. K., Chen, R. J., Ikamura, K., Gerber, G. K., Liang, I., Le, L. P., Ding, T., Parwani, A. V., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2312.07814 · Cited by 49

Innovative super-resolution in spatial transcriptomics: a transformer model exploiting histology images and spatial gene expression

Zhao, C., Xu, Z., Wang, X., Tao, S., MacDonald, W. A., He, K., Poholek, A. C., Chen, K., Huang, H., & Chen, W. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae052 · Cited by 44

Assessment of Pathology Domain-Specific Knowledge of ChatGPT and Comparison to Human Performance

Wang, A. Y., Lin, S., Tran, C., Homer, R., Wilsdon, D., Walsh, J. C., Goebel, E. A., Sansano, I., Sonawane, S. S., Cockenpot, V., et al. (2024) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2023-0296-oa · Cited by 43

Systematic evaluation of common natural language processing techniques to codify clinical notes

Tavabi, N., Singh, M., Pruneski, J. A., & Kiapour, A. M. (2024) · PLoS ONE · doi:10.1371/journal.pone.0298892 · Cited by 42

MGCT: Mutual-Guided Cross-Modality Transformer for Survival Outcome Prediction using Integrative Histopathology-Genomic Features

Liu, M., Liu, Y., Cui, H., Li, C., & Ma, J. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385897 · Cited by 42

Bridging bytes and biopsies: A comparative analysis of ChatGPT and histopathologists in pathology diagnosis and collaborative potential

Oon, M. L., Syn, N. L. X., Tan, C. L., Tan, K.-B., & Ng, S. (2023) · Histopathology · doi:10.1111/his.15100 · Cited by 41

Learning the histone codes with large genomic windows and three-dimensional chromatin interactions using transformer

Lee, D., Yang, J., & Kim, S. (2022) · Nature Communications · doi:10.1038/s41467-022-34152-5 · Cited by 40

An accurate prediction of the origin for bone metastatic cancer using deep learning on digital pathological images

Zhu, L., Shi, H., Wei, H., Wang, C., Shi, S., Zhang, F., Yan, R., Liu, Y., He, T., Wang, L., et al. (2022) · EBioMedicine · doi:10.1016/j.ebiom.2022.104426 · Cited by 40

AI models for protein design are driving antibody engineering

Chungyoun, M., & Gray, J. J. (2023) · Current Opinion in Biomedical Engineering · doi:10.1016/j.cobme.2023.100473 · Cited by 40

Clinical actionability of triaging DNA mismatch repair deficient colorectal cancer from biopsy samples using deep learning

Wu, J., Mei, W.-J., Xu, S., Ling, Y., Li, W., Kuang, J.-B., Li, H., Hui, H., Li, J., Cai, M., et al. (2022) · EBioMedicine · doi:10.1016/j.ebiom.2022.104120 · Cited by 40

Building the Model

Yang, H. S., Rhoads, D. D., Sepulveda, J. L., Zang, C., Chadburn, A., & Wang, F. (2022) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2021-0635-ra · Cited by 40

CpGPT: a Foundation Model for DNA MethylationPreprint

Camillo, L. P. D. L., Sehgal, R., Armstrong, J., Melnikas, M., Miller, H. E., Ding, J., Ferrucci, L., Lasky-Su, J. A., Higgins‐Chen, A., Horvath, S., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.24.619766 · Cited by 39

SAFRON: Stitching Across the Frontier Network for Generating Colorectal Cancer Histology Images

Deshpande, S., Minhas, F., Graham, S., & Rajpoot, N. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102337 · Cited by 38

Colorectal cancer lymph node metastasis prediction with weakly supervised transformer-based multi-instance learning

Tan, L., Li, H., Yu, J., Zhou, H., Wang, Z., Niu, Z., Li, J., & Li, Z. (2023) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-023-02799-x · Cited by 38

Organizational preparedness for the use of large language models in pathology informatics

Hart, S. N., Hoffman, N. G., Gershkovich, P., Christenson, C., McClintock, D. S., Miller, L. J., Jackups, R., Azimi, V., Spies, N. C., & Brodsky, V. (2023) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2023.100338 · Cited by 38

Deepfake Histologic Images for Enhancing Digital Pathology

Falahkheirkhah, K., Tiwari, S., Yeh, K. L., Gupta, S., Herrera-Hernandez, L. P., McCarthy, M. R., Jiménez, R. E., Cheville, J. C., & Bhargava, R. (2023) · Laboratory Investigation · doi:10.1016/j.labinv.2022.100006 · Cited by 38

VarChat: the generative AI assistant for the interpretation of human genomic variations

Paoli, F. D., Berardelli, S., Limongelli, I., Rizzo, E., & Zucca, S. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae183 · Cited by 38

Generation of synthetic whole-slide image tiles of tumours from RNA-sequencing data via cascaded diffusion models

Carrillo-Perez, F., Pizurica, M., Zheng, Y., Nandi, T. N., Madduri, R., Shen, J., & Gevaert, O. (2024) · Nature Biomedical Engineering · doi:10.1038/s41551-024-01193-8 · Cited by 37

Exploring the pitfalls of large language models: Inconsistency and inaccuracy in answering pathology board examination‐style questions

Koga, S. (2023) · Pathology International · doi:10.1111/pin.13382 · Cited by 36

Preliminary fatty liver disease grading using general-purpose online large language models: ChatGPT-4 or Bard?

Zhang, Y., Liu, H., Sheng, B., Tham, Y. C., & Ji, H. (2023) · Journal of Hepatology · doi:10.1016/j.jhep.2023.11.017 · Cited by 35

Are you sure it’s an artifact? Artifact detection and uncertainty quantification in histological images

Kanwal, N., López-Pérez, M., Kiraz, U., Zuiverloon, T. C. M., Molina, R., & Engan, K. (2023) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2023.102321 · Cited by 35

MetaTransformer: deep metagenomic sequencing read classification using self-attention models

Wichmann, A., Buschong, E., Müller, A., Jünger, D., Hildebrandt, A., Hankeln, T., & Schmidt, B. (2023) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqad082 · Cited by 34

DNA-Diffusion: Leveraging Generative Models for Controlling Chromatin Accessibility and Gene Expression via Synthetic Regulatory Elements

DaSilva, L. F., Senan, S., Patel, Z. M., Reddy, A. J., Gabbita, S., Nussbaum, Z., Córdova, C. M. V., Wenteler, A., Weber, N., Tunjic, T. M., et al. (2024) · doi:10.1101/2024.02.01.578352 · Cited by 32

Generative AI-Based Data Completeness Augmentation Algorithm for Data-Driven Smart Healthcare

Lan, G., Xiao, S., Yang, J., Wen, J., & Xi, M. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3327485 · Cited by 32

Large Language Models and Medical Education: Preparing for a Rapid Transformation in How Trainees Will Learn to be Doctors

Ravi, A., Neinstein, A., & Murray, S. G. (2023) · ATS Scholar · doi:10.34197/ats-scholar.2023-0036ps · Cited by 31

Tissue Artifact Segmentation and Severity Assessment for Automatic Analysis Using WSI

Hossain, M. S., Shahriar, G. M., Syeed, M. M. M., Uddin, M. F., Hasan, M., Hossain, M. S., & Bari, R. (2023) · IEEE Access · doi:10.1109/access.2023.3250556 · Cited by 31

Large Language Models Can be Lazy Learners: Analyze Shortcuts in In-Context Learning

Tang, R., Kong, D., Huang, L., & Xue, H. (2023) · Findings of the Association for Computational Linguistics: ACL 2023 · doi:10.18653/v1/2023.findings-acl.284 · Cited by 31

Stain normalization using score-based diffusion model through stain separation and overlapped moving window patch strategies

Jeong, J., Kim, K., Nam, Y., Cho, C. E., Go, H., & Kim, N. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106335 · Cited by 30

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 30

CycleGAN for virtual stain transfer: Is seeing really believing?

Vasiljević, J., Nisar, Z., Feuerhake, F., Wemmert, C., & Lampert, T. A. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102420 · Cited by 29

AI-Augmented Clinical Decision Support in a Patient-Centric Precision Oncology Registry

Shapiro, M. A., Stuhlmiller, T. J., Federowicz, B., Hoos, W., Wasserman, A., Kramer, G., Kaufman, Z., Chuyka, D., Friedland, J. C., Mahoney, B., et al. (2023) · AI in Precision Oncology · doi:10.1089/aipo.2023.0001 · Cited by 29

From Text to Tables: A Local Privacy Preserving Large Language Model for Structured Information Retrieval from Medical DocumentsPreprint

Wiest, I. C., Ferber, D., Zhu, J., van Treeck, M., Meyer, S. K., Juglan, R., Carrero, Z. I., Paech, D., Kleesiek, J., Ebert, M. P., et al. (2023) · medRxiv · doi:10.1101/2023.12.07.23299648 · Cited by 28

Evaluation of ChatGPT pathology knowledge using board-style questions

Geetha, S. D., Khan, A., Khan, A. I., Kannadath, B. S., & Vitkovski, T. (2023) · American Journal of Clinical Pathology · doi:10.1093/ajcp/aqad158 · Cited by 27

Multimodal Whole Slide Foundation Model for PathologyPreprint

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. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2411.19666 · Cited by 27

Enhanced cell segmentation with limited training datasets using cycle generative adversarial networks

Zargari, A., Topacio, B. R., Mashhadi, N., & Shariati, S. A. (2024) · iScience · doi:10.1016/j.isci.2024.109740 · Cited by 26

A Unified Framework for Microscopy Defocus Deblur with Multi-Pyramid Transformer and Contrastive Learning

Zhang, Y., Zheng, P., Yan, W., Fang, C., & Cheng, S. S. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01058 · Cited by 26

SciAssess: Benchmarking LLM Proficiency in Scientific Literature AnalysisPreprint

Cai, H., Cai, X., Chang, J., Li, S., Yao, L., Wang, C., Gao, Z., Wang, H., Li, Y., Lin, M., et al. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2403.01976 · Cited by 26

Hierarchical Transformer for Survival Prediction Using Multimodality Whole Slide Images and Genomics

Li, C., Zhu, X., Yao, J., & Huang, J. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956296 · Cited by 25

A multitask classification framework based on vision transformer for predicting molecular expressions of glioma

Xu, Q., Xu, Q., Shi, N., Li, D., Zhu, H., & Xu, K. (2022) · European Journal of Radiology · doi:10.1016/j.ejrad.2022.110560 · Cited by 24

Artificial Intelligence–Triaged 3-Dimensional Pathology to Improve Detection of Esophageal Neoplasia While Reducing Pathologist Workloads

Barner, L. A. E., Gao, G., Reddi, D., Lan, L., Burke, W., Mahmood, F., Grady, W. M., & Liu, J. T. C. (2023) · Modern Pathology · doi:10.1016/j.modpat.2023.100322 · Cited by 24

Efficient Supervised Pretraining of Swin-Transformer for Virtual Staining of Microscopy Images

Ma, J., & Chen, H. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3337253 · Cited by 24

ChatGPT’s innovative application in blood morphology recognition

Yang, W.-H., Yang, Y., & Chen, T. (2024) · Journal of the Chinese Medical Association · doi:10.1097/jcma.0000000000001071 · Cited by 24

TransformerFAM: Feedback attention is working memoryPreprint

Hwang, D., Wang, W.-R., Huo, Z., Sim, K., & Mengibar, P. M. (2024) · arXiv.org · doi:10.48550/arxiv.2404.09173 · Cited by 24

Artificial intelligence-based triage of large bowel biopsies can improve workflow

Mayall, F. G., Goodhead, M. D., de Mendonça, L., Brownlie, S. E., Anees, A., & Perring, S. (2023) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2022.100181 · Cited by 23

Diffusion-based generation of Histopathological Whole Slide Images at a Gigapixel scale

Harb, R., Pock, T., & Müller, H. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00505 · Cited by 23

Can Large Language Models Serve as Data Analysts? A Multi-Agent Assisted Approach for Qualitative Data AnalysisPreprint

Rasheed, Z., Waseem, M., Ahmad, A., Kemell, K., Wang, X., NguyenDuc, A., & Abrahamsson, P. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5149683 · Cited by 23

Path-BigBird: An AI-Driven Transformer Approach to Classification of Cancer Pathology Reports

Chandrashekar, M., Lyngaas, I., Hanson, H. A., Gao, S. B., Wu, X., & Gounley, J. P. (2024) · JCO Clinical Cancer Informatics · doi:10.1200/cci.23.00148 · Cited by 22

Ferrying Oncologists Across the Chasm of Interpreting Biomarker Testing Reports: Systematic Support Needed to Improve Care and Decrease Disparities

West, H., & Lovly, C. M. (2023) · JCO Oncology Practice · doi:10.1200/op.23.00010 · Cited by 21

Natural Language Programming in Medicine: Administering Evidence Based Clinical Workflows with Autonomous Agents Powered by Generative Large Language ModelsPreprint

Vaid, A., Lampert, J., Lee, J., Sawant, A. S., Apakama, D. U., Sakhuja, A., Soroush, A., Bick, S., Abbott, E., Gomez, H., et al. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2401.02851 · Cited by 21

Assessment of a large language model’s utility in helping pathology professionals answer general knowledge pathology questions

Benirschke, R. C., Wodskow, J., Prasai, K., Freeman, A., Lee, J. M., & Groth, J. (2023) · American Journal of Clinical Pathology · doi:10.1093/ajcp/aqad106 · Cited by 20

EpiGePT: a Pretrained Transformer model for epigenomicsPreprint

Gao, Z., Liu, Q., Zeng, W., Jiang, R., & Wong, W. H. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.15.549134 · Cited by 20

Performance of GPT-4 Vision on kidney pathology exam questions

Miao, J., Thongprayoon, C., Cheungpasitporn, W., & Cornell, L. D. (2024) · American Journal of Clinical Pathology · doi:10.1093/ajcp/aqae030 · Cited by 19

Towards efficient and effective unlearning of large language models for recommendation

Wang, H., Lin, J., Chen, B., Yang, Y., Tang, R., Zhang, W., & Yu, Y. H. (2024) · Frontiers of Computer Science · doi:10.1007/s11704-024-40044-2 · Cited by 19

Automated Claim Matching with Large Language Models: Empowering Fact-Checkers in the Fight Against Misinformation

Choi, E. C., & Ferrara, E. (2024) · Companion Proceedings of the ACM Web Conference 2024 · doi:10.1145/3589335.3651910 · Cited by 18

Using histopathology latent diffusion models as privacy-preserving dataset augmenters improves downstream classification performance

Niehues, J. M., Müller‐Franzes, G. A., Schirris, Y., Wagner, S. J., Jendrusch, M., Kloor, M., Pearson, A. T., Muti, H. S., Hewitt, K. J., Veldhuizen, G. P., et al. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108410 · Cited by 17

Mutual stain conversion between Giemsa and Papanicolaou in cytological images using cycle generative adversarial network

Teramoto, A., Yamada, A., Tsukamoto, T., Kiriyama, Y., Sakurai, E., Shiogama, K., Michiba, A., Imaizumi, K., Saito, K., & Fujita, H. (2021) · Heliyon · doi:10.1016/j.heliyon.2021.e06331 · Cited by 17

A Learning Program for Treatment Recommendations by Molecular Tumor Boards and Artificial Intelligence

Sunami, K., Naito, Y., Saigusa, Y., Amano, T., Ennishi, D., Imai, M., Kage, H., Kanai, M., Kenmotsu, H., Komine, K., et al. (2023) · JAMA Oncology · doi:10.1001/jamaoncol.2023.5120 · Cited by 17

Post-Processing Fairness Evaluation of Federated Models: An Unsupervised Approach in Healthcare

Siniosoglou, I., Argyriou, V., Sarigiannidis, P. G., Λάγκας, Θ., Sarigiannidis, A. G., Goudos, S. K., & Wan, S. (2023) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2023.3269767 · Cited by 16

‘Without these tools, I’d be lost’: how generative AI aids in accessibility

Heidt, A. (2024) · Nature · doi:10.1038/d41586-024-01003-w · Cited by 16

Image-to-image translation for automatic ink removal in whole slide images

Jiang, J., Prodduturi, N., Chen, D. J., Gu, Q., Flotte, T. J., Feng, Q., & Hart, S. N. (2020) · Journal of medical imaging · doi:10.1117/1.jmi.7.5.057502 · Cited by 16

Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking

Zelikman, E., Harik, G., Shao, Y., Jayasiri, V., Haber, N., & Goodman, N. D. (2024) · arXiv · doi:10.48550/arxiv.2403.09629 · Cited by 16

DiffInfinite: Large Mask-Image Synthesis via Parallel Random Patch Diffusion in HistopathologyPreprint

Aversa, M., Nobis, G., Hägele, M., Standvoss, K., Chirica, M., Murray‐Smith, R., Alaa, A. M., Ruff, L., Ivanova, D., Samek, W., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2306.13384 · Cited by 16

HoechstGAN: Virtual Lymphocyte Staining Using Generative Adversarial Networks

Wölflein, G., Um, I. H., Harrison, D. J., & Arandjelović, O. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00497 · Cited by 15

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

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

Commercially Available Artificial Intelligence Solutions for Gynaecologic Cytology Screening and Their Integration Into Clinical Workflow

Chong, Y., & Bychkov, A. (2025) · Cytopathology · doi:10.1111/cyt.70023 · Cited by 12

Evaluation 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

Interobserver Agreement in Immunohistochemical Evaluation of Folate Receptor Alpha (FRα) in Ovarian Cancer: A Multicentre Study

Zannoni, 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

Generative artificial intelligence technologies in pathology

Ullah, E. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-40383-5.00013-3

Multimodal Generative AI for Anatomic Pathology—A Review of Current Applications to Envisage the Future Direction

Ullah, E., Baig, M. M., Waqas, A., Rasool, G., Singh, R. P., Shandilya, A., GholamHossieni, H., & Parwani, A. V. (2025) · Advances in Anatomic Pathology · doi:10.1097/pap.0000000000000498 · Cited by 9

Harnessing the Power of Generative Artificial Intelligence in Pathology Education: Opportunities, Challenges, and Future Directions

Cecchini, M. J., Borowitz, M. J., Glassy, E. F., Gullapalli, R. R., Hart, S. N., Hassell, L. A., Homer, R., Jackups, R., McNeal, J. L., & Anderson, S. R. (2024) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2024-0187-ra · Cited by 23

Artificial intelligence in anatomical pathology: building a strong foundation for precision medicine

Meroueh, C., & Chen, Z. E. (2022) · Human Pathology · doi:10.1016/j.humpath.2022.07.008 · Cited by 42

Generative Artificial Intelligence in Pathology and Medicine: A Deeper Dive

Rashidi, H. H., Pantanowitz, J., Chamanzar, A., Fennell, B. D., Wang, Y., Gullapalli, R. R., Tafti, A. P., Deebajah, M., Albahra, S., Glassy, E. F., et al. (2024) · Modern Pathology · doi:10.1016/j.modpat.2024.100687 · Cited by 42