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Research output

Selected publications

A curated selection of 21 first-, corresponding-, and co-authored works spanning auditable medical AI, generative models, multimodal reasoning, and robust clinical machine learning. An asterisk denotes equal contribution.

2026

  1. Latest · Open access

    CLEAR: an auditable foundation model for radiology grounded in clinical concepts

    Tianyu Han*, Riga Wu*, Yu Tian, Firas Khader, Lisa C. Adams, Keno K. Bressem, Christos Davatzikos, Jakob N. Kather, Li Shen, David A. Mankoff, Eduardo M. Barbosa Jr, Daniel Truhn

    Nature Biomedical Engineering · 2026

    An auditable foundation model with released code, weights, and concept resources for academic research. It represents chest X-rays through 368,294 radiological observations, enabling concept-level auditing, confounder discovery, and intervention.

    Paper DOI Code Model

  2. Counterfactual diffusion models provide interpretable explanations of artificial intelligence models in pathology

    L. Žigutytė, T. Lenz, T. Han, N. G. Reitsam, S. Foersch, K. J. Hewitt, et al.

    Cancer Research · 2026

    Paper

2025

  1. Medical slice transformer for improved diagnosis and explainability on 3D medical images with DINOv2

    G. Müller-Franzes, F. Khader, R. Siepmann, T. Han, J. N. Kather, S. Nebelung, D. Truhn

    Scientific Reports · 2025

    Paper

  2. Evaluating the effectiveness of biomedical fine-tuning for large language models on clinical tasks

    F. J. Dorfner, A. Dada, F. Busch, T. Han, D. Truhn, J. Kleesiek, M. Sushil, J. Lammert, M. R. Makowski, L. C. Adams, K. K. Bressem

    Journal of the American Medical Informatics Association · 2025

    Paper Correction

  3. Accelerating breast MRI acquisition with generative AI models

    A. Okolie, T. Dirrichs, L. C. Huck, S. Nebelung, S. Tayebi Arasteh, T. Nolte, T. Han, C. K. Kuhl, D. Truhn

    European Radiology · 2025

    Paper

2024

  1. Reconstruction of patient-specific confounders in AI-based radiologic image interpretation using generative pretraining

    T. Han, L. Žigutytė, L. Huck, M. S. Huppertz, R. Siepmann, et al.

    Cell Reports Medicine · 2024

    Paper

  2. Medical large language models are susceptible to targeted misinformation attacks

    T. Han, S. Nebelung, F. Khader, T. Wang, G. Müller-Franzes, et al.

    npj Digital Medicine · 2024

    Paper

  3. Comparative analysis of multimodal large language model performance on clinical vignette questions

    T. Han*, L. C. Adams*, K. K. Bressem, F. Busch, S. Nebelung, D. Truhn

    JAMA · 2024

    Paper

  4. Large language models streamline automated machine learning for clinical studies

    S. Tayebi Arasteh, T. Han, M. Lotfinia, C. Kuhl, J. N. Kather, D. Truhn, S. Nebelung

    Nature Communications · 2024

    Paper Code

  5. Encrypted federated learning for secure decentralized collaboration in cancer image analysis

    D. Truhn, S. Tayebi Arasteh, O. L. Saldanha, G. Müller-Franzes, F. Khader, …, T. Han, et al.

    Medical Image Analysis · 2024

    Paper

  6. Integrating text and image analysis: exploring GPT-4V’s capabilities in advanced radiological applications across subspecialties

    F. Busch, T. Han, M. R. Makowski, D. Truhn, K. K. Bressem, L. C. Adams

    Journal of Medical Internet Research · 2024

    Paper Correction (2024) Correction (2026)

  7. On instabilities of unsupervised denoising diffusion models in magnetic resonance imaging reconstruction

    T. Han, S. Nebelung, F. Khader, J. N. Kather, D. Truhn

    MICCAI · 2024

    Paper

2023

  1. Using machine learning to reduce the need for contrast agents in breast MRI through synthetic images

    G. Müller-Franzes, L. Huck, S. Tayebi Arasteh, F. Khader, T. Han, V. Schulz, E. Dethlefsen, J. N. Kather, S. Nebelung, T. Nolte, et al.

    Radiology · 2023

    Paper

  2. Multimodal deep learning for integrating chest radiographs and clinical parameters: a case for transformers

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

    Radiology · 2023

    Paper

  3. Artificial intelligence for clinical interpretation of bedside chest radiographs

    F. Khader, T. Han, G. Müller-Franzes, L. Huck, P. Schad, S. Keil, E. Barzakova, M. Schulze-Hagen, F. Pedersoli, V. Schulz, et al.

    Radiology · 2023

    Paper

  4. Denoising diffusion probabilistic models for 3D medical image generation

    F. Khader, G. Müller-Franzes, S. Tayebi Arasteh, T. Han, C. Haarburger, M. Schulze-Hagen, P. Schad, S. Engelhardt, B. Baeßler, S. Foersch, et al.

    Scientific Reports · 2023

    Paper

  5. MedAlpaca: an open-source collection of medical conversational AI models and training data

    T. Han, L. C. Adams, J.-M. Papaioannou, P. Grundmann, T. Oberhauser, et al.

    arXiv preprint · 2023

    Preprint

2022

  1. Image prediction of disease progression for osteoarthritis by style-based manifold extrapolation

    T. Han, J. N. Kather, F. Pedersoli, M. Zimmermann, S. Keil, et al.

    Nature Machine Intelligence · 2022

    Paper

  2. Adversarial attacks and adversarial robustness in computational pathology

    N. Ghaffari Laleh, D. Truhn, G. P. Veldhuizen, T. Han, M. van Treeck, R. D. Buelow, R. Langer, B. Dislich, P. Boor, V. Schulz, J. N. Kather

    Nature Communications · 2022

    Paper

2021

  1. Advancing diagnostic performance and clinical usability of neural networks via adversarial training and dual batch normalization

    T. Han, S. Nebelung, F. Pedersoli, M. Zimmermann, M. Schulze-Hagen, et al.

    Nature Communications · 2021

    Paper

2020

  1. Breaking medical data sharing boundaries by using synthesized radiographs

    T. Han, S. Nebelung, C. Haarburger, N. Horst, S. Reinartz, et al.

    Science Advances · 2020

    Paper

See the complete record on Google Scholar or NCBI My Bibliography.