Kather, Jakob Nikolas

Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer

Kather, J. N., Pearson, A. T., Halama, N., Jäger, D., Krause, J., Loosen, S. H., Marx, A., Boor, P., Tacke, F., Neumann, U. P., et al. (2019) · Nature Medicine

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 1,458

Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study

Kather, J. N., Krisam, J., Charoentong, P., Luedde, T., Herpel, E., Weis, C.-A., Gaiser, T., Marx, A., Valous, N. A., Ferber, D., et al. (2019) · PLOS Medicine

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 1,082

The future landscape of large language models in medicine

Clusmann, J., Kolbinger, F. R., Muti, H. S., Carrero, Z. I., Eckardt, J.-N., Laleh, N. G., Löffler, C. M. L., Schwarzkopf, S.-C., Unger, M., Veldhuizen, G. P., et al. (2023) · Communications Medicine

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 1,045

Deep learning in cancer pathology: a new generation of clinical biomarkers

Echle, A., Rindtorff, N. T., Brinker, T. J., Luedde, T., Pearson, A. T., & Kather, J. N. (2021) · British Journal of Cancer

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 667

Multi-class texture analysis in colorectal cancer histology

Kather, J. N., Weis, C.-A., Bianconi, F., Melchers, S. M., Schad, L. R., Gaiser, T., Marx, A., & Zöllner, F. G. (2016) · Scientific Reports

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 578

Artificial intelligence in histopathology: enhancing cancer research and clinical oncology

Shmatko, A., Ghaffari Laleh, N., Gerstung, M., & Kather, J. N. (2022) · Nature Cancer

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 446

Clinical-Grade Detection of Microsatellite Instability in Colorectal Tumors by Deep Learning

Echle, A., Grabsch, H. I., Quirke, P., van den Brandt, P. A., West, N. P., Hutchins, G. G. A., Heij, L. R., Tan, X., Richman, S. D., Krause, J., et al. (2020) · Gastroenterology

Genetic factors in colorectal cancerAI in cancer detection

Cited by 376

Current applications and challenges in large language models for patient care: a systematic review

Busch, F., Hoffmann, L., Rueger, C., van Dijk, E. H., Kader, R., Ortiz-Prado, E., Makowski, M. R., Saba, L., Hadamitzky, M., Kather, J. N., et al. (2025) · Communications Medicine

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 358

Artificial Intelligence for Predictive Biomarker Discovery in Immuno-Oncology: A Systematic Review

Prelaj, A., Miskovic, V., Zanitti, M., Trovo, F., Genova, C., Viscardi, G., Rebuzzi, S. E., Mazzeo, L., Provenzano, L., Kosta, S., et al. (2024) · Annals of Oncology

Cancer Immunotherapy and BiomarkersRadiomics and Machine Learning in Medical Imaging

Cited by 340

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

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 330

Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets

Schulz, M.-A., Yeo, B. T. T., Vogelstein, J. T., Mourao-Miranada, J., Kather, J. N., Kording, K., Richards, B., & Bzdok, D. (2020) · Nature Communications

Machine Learning in HealthcareNeural Networks and Applications

Cited by 315

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

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 275

Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts

Haggenmüller, S., Maron, R. C., Hekler, A., Utikal, J. S., Barata, C., Barnhill, R. L., Beltraminelli, H., Berking, C., Betz-Stablein, B., Blum, A., et al. (2021) · European Journal of Cancer

Cutaneous Melanoma Detection and ManagementAI in cancer detection

Cited by 267

Multistain deep learning for prediction of prognosis and therapy response in colorectal cancer

Foersch, S., Glasner, C., Woerl, A.-C., Eckstein, M., Wagner, D.-C., Schulz, S., Kellers, F., Fernandez, A., Tserea, K., Kloth, M., et al. (2023) · Nature Medicine

AI in cancer detectionCancer Immunotherapy and Biomarkers

Cited by 251

ESMO guidance for reporting oncology real-world evidence (GROW)

Castelo-Branco, L., Pellat, A., Martins-Branco, D., Valachis, A., Derksen, J. W. G., Suijkerbuijk, K. P. M., Dafni, U., Dellaporta, T., Vogel, A., Prelaj, A., et al. (2023) · Annals of Oncology

Meta-analysis and systematic reviewsClinical practice guidelines implementation

Cited by 247

Swarm learning for decentralized artificial intelligence in cancer histopathology

Saldanha, O. L., Quirke, P., West, N. P., James, J. A., Loughrey, M. B., Grabsch, H. I., Salto-Tellez, M., Alwers, E., Cifci, D., Ghaffari Laleh, N., et al. (2022) · Nature Medicine

AI in cancer detectionArtificial Intelligence in Healthcare and Education

Cited by 227

Development and validation of an autonomous artificial intelligence agent for clinical decision-making in oncology

Ferber, D., El Nahhas, O. S. M., Wölflein, G., Wiest, I. C., Clusmann, J., Leßmann, M.-E., Foersch, S., Lammert, J., Tschochohei, M., Jäger, D., et al. (2025) · Nature Cancer

Artificial Intelligence in Healthcare and EducationAI in cancer detection

Cited by 175

Reporting guidelines in medical artificial intelligence: a systematic review and meta-analysis

Kolbinger, F. R., Veldhuizen, G. P., Zhu, J., Truhn, D., & Kather, J. N. (2024) · Communications Medicine

Artificial Intelligence in Healthcare and EducationMeta-analysis and systematic reviews

Cited by 160

Large language models for structured reporting in radiology: past, present, and future

Busch, F., Hoffmann, L., dos Santos, D. P., Makowski, M. R., Saba, L., Prucker, P., Hadamitzky, M., Navab, N., Kather, J. N., Truhn, D., et al. (2024) · European Radiology

Artificial Intelligence in Healthcare and EducationRadiology practices and education

Cited by 134

An MRI deep learning model predicts outcome in rectal cancer

Jiang, X., Zhao, H., Saldanha, O. L., Nebelung, S., Kuhl, C., Amygdalos, I., Lang, S. A., Wu, X., Meng, X., Truhn, D., et al. (2023) · Radiology

Radiomics and Machine Learning in Medical ImagingColorectal Cancer Surgical Treatments

Cited by 127

Development and validation of deep learning classifiers to detect Epstein-Barr virus and microsatellite instability status in gastric cancer: a retrospective multicentre cohort study

Muti, H. S., Heij, L. R., Keller, G., Kohlruss, M., Langer, R., Dislich, B., Cheong, J.-H., Kim, Y.-W., Kim, H., Kook, M.-C., et al. (2021) · The Lancet Digital Health

AI in cancer detectionGastric Cancer Management and Outcomes

Cited by 126

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

Artificial Intelligence in Healthcare and EducationAI in cancer detection

Cited by 124

Large language models streamline automated machine learning for clinical studies

Tayebi Arasteh, S., Han, T., Lotfinia, M., Kuhl, C., Kather, J. N., Truhn, D., & Nebelung, S. (2024) · Nature Communications

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 114

Artificial intelligence for detection of microsatellite instability in colorectal cancer—a multicentric analysis of a pre-screening tool for clinical application

Echle, A., Ghaffari Laleh, N., Quirke, P., Grabsch, H. I., Muti, H. S., Saldanha, O. L., Brockmoeller, S. F., van den Brandt, P. A., Hutchins, G. G. A., Richman, S. D., et al. (2022) · ESMO Open

AI in cancer detectionGenetic factors in colorectal cancer

Cited by 111