Truhn, Daniel

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

Leveraging gpt-4 for post hoc transformation of free-text radiology reports into structured reporting: a multilingual feasibility study

Adams, L. C., Truhn, D., Busch, F., Kader, A., Niehues, S. M., Makowski, M. R., & Bressem, K. K. (2023) · Radiology

Artificial Intelligence in Healthcare and EducationBiomedical Text Mining and Ontologies

Cited by 339

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

Radiomic versus Convolutional Neural Networks Analysis for Classification of Contrast-enhancing Lesions at Multiparametric Breast MRI

Truhn, D., Schrading, S., Haarburger, C., Schneider, H., Merhof, D., & Kuhl, C. (2019) · Radiology

Radiomics and Machine Learning in Medical ImagingMRI in cancer diagnosis

Cited by 273

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

Radiomics feature reproducibility under inter-rater variability in segmentations of CT images

Haarburger, C., Müller-Franzes, G., Weninger, L., Kuhl, C., Truhn, D., & Merhof, D. (2020) · Scientific Reports

Radiomics and Machine Learning in Medical ImagingMedical Image Segmentation Techniques

Cited by 139

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

Multinational Attitudes Toward AI in Health Care and Diagnostics Among Hospital Patients

Busch, F., Hoffmann, L., Xu, L., Zhang, L. J., Hu, B., García-Juárez, I., Toapanta-Yanchapaxi, L. N., Gorelik, N., Gorelik, V., Rodriguez-Granillo, G. A., et al. (2025) · JAMA Network Open

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 124

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

Global cross-sectional student survey on AI in medical, dental, and veterinary education and practice at 192 faculties

Busch, F., Hoffmann, L., Truhn, D., Ortiz-Prado, E., Makowski, M. R., Bressem, K. K., Adams, L. C., COMFORT Consortium, Abdala, N., Navarro, Á. A., et al. (2024) · BMC Medical Education

Artificial Intelligence in Healthcare and EducationInnovations in Medical Education

Cited by 108

End-to-end prognostication in colorectal cancer by deep learning: a retrospective, multicentre study

Jiang, X., Hoffmeister, M., Brenner, H., Muti, H. S., Yuan, T., Foersch, S., West, N. P., Brobeil, A., Jonnagaddala, J., Hawkins, N., et al. (2024) · The Lancet Digital Health

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 108

Multimodal Deep Learning for Integrating Chest Radiographs and Clinical Parameters: A Case for Transformers

Khader, F., Müller-Franzes, G., Wang, T., Han, T., Tayebi Arasteh, S., Haarburger, C., Stegmaier, J., Bressem, K., Kuhl, C., Nebelung, S., et al. (2023) · Radiology

Multimodal Machine Learning ApplicationsMachine Learning in Healthcare

Cited by 107

Large language models should be used as scientific reasoning engines, not knowledge databases

Truhn, D., Reis-Filho, J. S., & Kather, J. N. (2023) · Nature Medicine

Artificial Intelligence in Healthcare and EducationMultimodal Machine Learning Applications

Cited by 102

GPT-4 for Information Retrieval and Comparison of Medical Oncology Guidelines

Ferber, D., Wiest, I. C., Wölflein, G., Ebert, M. P., Beutel, G., Eckardt, J.-N., Truhn, D., Springfeld, C., Jäger, D., & Kather, J. N. (2024) · NEJM AI

Artificial Intelligence in Healthcare and EducationClinical practice guidelines implementation

Cited by 92

Comparative Analysis of Multimodal Large Language Model Performance on Clinical Vignette Questions

Han, T., Adams, L. C., Bressem, K. K., Busch, F., Nebelung, S., & Truhn, D. (2024) · JAMA

Artificial Intelligence in Healthcare and EducationMultimodal Machine Learning Applications

Cited by 83

International pharmacy students' perceptions towards artificial intelligence in medicine—A multinational, multicentre cross‐sectional study

Busch, F., Hoffmann, L., Truhn, D., Palaian, S., Alomar, M., Shpati, K., Makowski, M. R., Bressem, K. K., & Adams, L. C. (2024) · British Journal of Clinical Pharmacology

Artificial Intelligence in Healthcare and EducationInnovations in Medical Education

Cited by 80

Benchmarking foundation models as feature extractors for weakly supervised computational pathology

Neidlinger, P., El Nahhas, O. S. M., Muti, H. S., Lenz, T., Hoffmeister, M., Brenner, H., van Treeck, M., Langer, R., Dislich, B., Behrens, H. M., et al. (2025) · Nature Biomedical Engineering

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 80

Large language models and multimodal foundation models for precision oncology

Truhn, D., Eckardt, J.-N., Ferber, D., & Kather, J. N. (2024) · npj Precision Oncology

Artificial Intelligence in Healthcare and EducationMultimodal Machine Learning Applications

Cited by 79

A pilot study on the efficacy of GPT-4 in providing orthopedic treatment recommendations from MRI reports

Truhn, D., Weber, C. D., Braun, B. J., Bressem, K., Kather, J. N., Kuhl, C., & Nebelung, S. (2023) · Scientific Reports

Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

Cited by 71

Medical large language models are susceptible to targeted misinformation attacks

Han, T., Nebelung, S., Khader, F., Wang, T., Müller-Franzes, G., Kuhl, C., Försch, S., Kleesiek, J., Haarburger, C., Bressem, K. K., et al. (2024) · npj Digital Medicine

Adversarial Robustness in Machine LearningArtificial Intelligence in Healthcare and Education

Cited by 62