Clinical Learning Systems

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Soroosh Tayebi Arasteh

Dr.-Ing. Dr. rer. medic. Soroosh Tayebi Arasteh

Academic CV
  • Since 10/2025, Group Leader, University Hospital RWTH Aachen and RWTH Aachen University, Aachen, Germany
  • 03/2025 to 06/2026, Affiliated Postdoctoral Scholar, Stanford University, Departments of Urology and Radiology, Stanford, USA
  • 08/2024 to 09/2025, Postdoctoral Researcher and Lecturer, Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
  • 2024, Doctor of Theoretical Medicine (Dr. rer. medic.), RWTH Aachen University, Aachen, Germany
  • 2024, Doctor of Engineering (Dr.-Ing.) in Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
  • 05/2021 to 10/2021, Medical AI and Computer Vision Engineer, Virtonomy GmbH, Munich, Germany
  • 2021, M.Sc. thesis, Medical Image and Data Processing, Harvard Medical School, Boston, USA
  • 2021, M.Sc. in Electrical Engineering, Communications and Multimedia, Friedrich-Alexander-Universität Erlangen-Nürnberg, magna cum laude, Erlangen, Germany
  • 03/2020 to 04/2021, Deep Learning Engineer, Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany
  • 2017, B.Sc. in Electrical Engineering, Bu-Ali Sina University, Hamadan, Iran
Publications

2026

  • V. M. Rao*, E. Chen*, A. Saenz*, O. Banerjee, H. Marklund, L. Bijnens, S. Eun Kim, E. Zucker, S. Shelmerdine, J. Palacio, K. Ryan Kleinschmidt, S. Suryadevara, S. Sai Dinesh Jaliparthi, J. Lu, R. Akula, M. Scala, J. F. Niedermair, N. Stranger, S. Tschauner, G. M. Schmölzer, M. Jose Veloza Vega, J. Sebastián Osorio Valencia, O. Jaramillo Robledo, O. Julian López Uribe, S. Afat, T. Küstner, S. Tayebi Arasteh, D. Truhn, K. L. Pomykala, J. Kleesiek, N. Himayati Afifah, R. M. Irfan, R. Shahriarirad, A. Erfani, I. Amirav, M. Be’er, Y. Mitsuyama, D. Ueda, A. S. Abi-Ghanem, S. Al Zakleet, M. Abdelhak Shaban Mohamed, W. Irfan Bin W Mustapha, N. Nizami, R. Khan, K. Al-Kharazi, Z. Iqbal, A. Al Shahrani, A. Kashgari, T. Prodanovic, N. Prodanovic, D. Shi Wei Lim, J. Thomas Patrick Decourcy Hallinan, P. Nongena, M. Louise Cooke, M. Bret-Zurita, A. Alonso-Ojembarrena, A. Nemes, Y. Lin, W. Lin, W. Fang, C. Lin, A. Mahayosnond, S. Sriswasdi, P. Khumrin, S. Tanmuk, D. Arman, S. Cömert, S. Alan, U. Ayse Tandircioglu, D. Andrew Mong, E. Chung, M. Sadler, M. P. McBee, R. H. Jones, and P. Rajpurkar. “Repurposing adult AI radiograph models for neonatal care through continual learning: an international multicentre study.” Nature Biomedical Engineering, 2026.[URL]
  • S. Tayebi Arasteh, M. Mohammadi, S. Nebelung, and D. Truhn. “Differential privacy representation geometry for medical image analysis.” MICCAI 2026.[URL]
  • V. Negi, L. Sorokin, S. Tayebi Arasteh, and A. Stocco. “Automated evaluation of multi-turn dialogues in in-car conversational assistants.” IEEE International Conference on Intelligent Transportation Systems (ITSC) 2026.[URL]
  • S. Tayebi Arasteh, M. Shaigan, C. Kuhl, J. Nikolas Kather, S. Nebelung, and D. Truhn. “Resolution-dependent self-supervised transfer in chest radiograph classification.” Communications Medicine, 2026.[URL]
  • M. Lotfinia, D. Truhn, A. Maier, and S. Tayebi Arasteh. “FRAME: separating sampling variation from performance disparities in medical image analysis.” arXiv preprint, 2026.[URL]
  • M. Farajiamiri, J. Sopa, S. Afza, L. Adams, F. Barajas Ordonez, T. Nguyen, M. Lotfinia, S. Wind, K. Bressem, S. Nebelung, D. Truhn, and S. Tayebi Arasteh. “Retrieval-augmented reasoning reshapes collective reliability in text-based radiology question answering.” Patterns, Cell Press, 2026.[URL]
  • M. Alhaskir, H. Haven, M. Langner, S. Tayebi Arasteh, H. Heidemeyer, N. Sacic, R. W. Majeed, A. Peters, N. Müller, M. Fries, R. Otto, K. O. Hensel, C. Plata, T. Peters, S. Ostermann, Y. Haven, M. Hertwig, J. Unterkofler, R. Röhrig, and J. Bienzeisler. “Automated feedback generation in paediatric simulation training: a prospective validation of AI-generated structured feedback reports.” Research Square preprint, 2026.[URL]
  • S. Tayebi Arasteh, S. Ziegelmayer, M. Lotfinia, L. Adams, S. Nebelung, J. Nikolas Kather, and D. Truhn. “Medical foundation models converge less under label supervision.” arXiv preprint, 2026.[URL]
  • S. Wind, J. Sopa, L. Schmid, Q. Jackl, S. Kiefer, F. Wu, M. Mayr, H. Köstler, G. Wellein, A. Maier, and S. Tayebi Arasteh. “SteuerLLM: local specialized large language model for German tax law analysis.” Scientific Reports, 2026.[URL]
  • M. Lotfinia, S. Ziegelmayer, L. Adams, T. Nguyen, D. Truhn, A. Maier, and S. Tayebi Arasteh. “Vision-language models for chest radiography do not always need the image.” arXiv preprint, 2026.[URL]
  • S. Tayebi Arasteh and D. Truhn. “When framing shapes the answer: cognitive bias and large language model reliability in radiology.” Radiology: Artificial Intelligence, 2026.[URL]
  • S. Wind, T. Nguyen, J. Sopa, M. Lotfinia, S. Bickelhaup, M. Uder, H. Köstler, G. Wellein, S. Nebelung, D. Truhn, A. Maier, and S. Tayebi Arasteh. “Safety and accuracy follow different scaling laws in clinical large language models.” arXiv preprint, 2026.[URL]
  • S. Tayebi Arasteh. “A privacy stack for speech-based AI in digital health.” npj Digital Public Health, 2026.[URL]
  • S. Tayebi Arasteh, M. Joodaki, M. Lotfinia, S. Nebelung, and D. Truhn. “Case-grounded evidence verification: a framework for constructing evidence-sensitive supervision.” arXiv preprint, 2026.[URL]
  • I. Mese, T. Akinci D’Antonoli, C. Bluethgen, ..., S. Tayebi Arasteh, ..., W. Kim, and B. Kocak. “Reporting checklist for foundation and large language models in medical research (REFINE): an international consensus guideline.” Diagnostic and Interventional Radiology, 2026.[URL]
  • S. Tayebi Arasteh, M. Farajiamiri, M. Lotfinia, B. Hinrichs-Puladi, J. Bienzeisler, M. Alhaskir, M. Rusu, C. Kuhl, S. Nebelung, and D. Truhn. “The pretraining domain outweighs the training objective in setting the privacy-utility trade-off of differentially private medical image analysis.” arXiv preprint, 2026.[URL]
  • M. Mohammadi, M. Vejdanihemmat, M. Lotfinia, M. Rusu, D. Truhn, A. Maier, and S. Tayebi Arasteh. “Differential privacy for medical deep learning: methods, tradeoffs, and deployment implications.” npj Digital Medicine, 2026.[URL]

2025

  • S. Wind, J. Sopa, D. Truhn, M. Lotfinia, T. Nguyen, K. Bressem, L. Adams, M. Rusu, H. Köstler, G. Wellein, A. Maier, and S. Tayebi Arasteh. “Multi-step retrieval and reasoning improves radiology question answering with large language models.” npj Digital Medicine, 2025.[URL]
  • S. Tayebi Arasteh, M. Lotfinia, P. Andrea Perez-Toro, T. Arias-Vergara, M. Ranji, J. Rafael Orozco-Arroyave, M. Schuster, A. Maier, and S. Hee Yang. “Differential privacy enables fair and accurate AI-based analysis of speech disorders while protecting patient data.” npj Artificial Intelligence, 2025.[URL]
  • L. Buess, M. Keicher, N. Navab, A. Maier, and S. Tayebi Arasteh. “From large language models to multimodal AI: a scoping review on the potential of generative AI in medicine.” Biomedical Engineering Letters, 2025.[URL]
  • T. Arias-Vergara, L. Buess, N. Vysotskaya, S. Tayebi Arasteh, J. Rafael Orozco-Arroyave, M. Schuster, E. Noeth, A. Maier, and P. Andrea Perez-Toro. “Acoustic-driven generation of pathological speech reports using large language models.” Research Square preprint, 2025.[URL]
  • M. Lotfinia, A. Tayebiarasteh, S. Samiei, M. Joodaki, and S. Tayebi Arasteh. “Boosting multi-demographic federated learning for chest radiograph analysis using general-purpose self-supervised representations.” European Journal of Radiology Artificial Intelligence, 2025.[URL]
  • S. Tayebi Arasteh, M. Lotfinia, K. Bressem, R. Siepmann, L. Adams, D. Ferber, C. Kuhl, J. Nikolas Kather, S. Nebelung, and D. Truhn. “RadioRAG: online retrieval-augmented generation for radiology question answering.” Radiology: Artificial Intelligence, 2025.[URL]
  • S. Tayebi Arasteh, S. Afza, T. Nguyen, L. Buess, M. Parvin, T. Arias-Vergara, P. Andrea Perez-Toro, H. Ching Hung, M. Lotfinia, T. Gorges, E. Noeth, M. Schuster, S. Hee Yang, and A. Maier. “Perceptual implications of automatic anonymization in pathological speech.” arXiv preprint, 2025.[URL]
  • A. Okolie, T. Dirrichs, L. Charlotte Huck, S. Nebelung, S. Tayebi Arasteh, T. Nolte, T. Han, C. Katharina Kuhl, and D. Truhn. “Accelerating breast MRI acquisition with generative AI models.” European Radiology, 2025.[URL]

2024

  • S. Tayebi Arasteh, R. Siepmann, M. Huppertz, M. Lotfinia, B. Puladi, C. Kuhl, D. Truhn, and S. Nebelung. “The treasure trove hidden in plain sight: the utility of GPT-4 in chest radiograph evaluation.” Radiology, 2024.[URL]
  • S. Tayebi Arasteh, T. Arias-Vergara, P. Andrea Pérez-Toro, T. Weise, K. Packhäuser, M. Schuster, E. Noeth, A. Maier, and S. Hee Yang. “Addressing challenges in speaker anonymization to maintain utility while ensuring privacy of pathological speech.” Communications Medicine, 2024.[URL]
  • G. Müller-Franzes, F. Khader, S. Tayebi Arasteh, L. Huck, M. Bode, T. Han, T. Lemainque, J. Nikolas Kather, S. Nebelung, C. Kuhl, and D. Truhn. “Intraindividual comparison of different methods for automated BPE assessment at breast MRI: a call for standardization.” Radiology, 2024.[URL]
  • S. Tayebi Arasteh, A. Ziller, C. Kuhl, M. Makowski, S. Nebelung, R. Braren, D. Rueckert, D. Truhn, and G. Kaissis. “Preserving fairness and diagnostic accuracy in private large-scale AI models for medical imaging.” Communications Medicine, 2024.[URL]
  • S. Tayebi Arasteh, T. Han, M. Lotfinia, C. Kuhl, J. Nikolas Kather, D. Truhn, and S. Nebelung. “Large language models streamline automated machine learning for clinical studies.” Nature Communications, 2024.[URL]
  • S. Tayebi Arasteh, L. Misera, J. Nikolas Kather, D. Truhn, and S. Nebelung. “Enhancing diagnostic deep learning via self-supervised pretraining on large-scale, unlabeled non-medical images.” European Radiology Experimental, 2024.[URL]
  • D. Truhn*, S. Tayebi Arasteh*, et al. “Encrypted federated learning for secure decentralized collaboration in cancer image analysis.” Medical Image Analysis, 2024.[URL]
  • S. Tayebi Arasteh, M. Lotfinia, T. Nolte, M. Sähn, P. Isfort, C. Kuhl, S. Nebelung, G. Kaissis, and D. Truhn. “Securing collaborative medical AI by using differential privacy: domain transfer for classification of chest radiographs.” Radiology: Artificial Intelligence, 2024.[URL]

2023

  • S. Tayebi Arasteh, C. Kuhl, M. Saehn, P. Isfort, D. Truhn, and S. Nebelung. “Enhancing domain generalization in the AI-based analysis of chest radiographs with federated learning.” Scientific Reports, 2023.[URL]
  • S. Tayebi Arasteh, T. Weise, M. Schuster, E. Noeth, A. Maier, and S. Hee Yang. “The effect of speech pathology on automatic speaker verification: a large-scale study.” Scientific Reports, 2023.[URL]
  • S. Tayebi Arasteh, J. Romanowicz, D. F. Pace, P. Golland, A. J. Powell, A. K. Maier, D. Truhn, T. Brosch, J. Weese, M. Lotfinia, R. J. van der Geest, and M. H. Moghari. “Automated segmentation of 3D cine cardiovascular magnetic resonance imaging.” Frontiers in Cardiovascular Medicine, 2023.[URL]
  • F. Khader, G. Müller-Franzes, T. Wang, T. Han, S. Tayebi Arasteh, C. Haarburger, J. Stegmaier, K. Bressem, C. Kuhl, S. Nebelung, J. Nikolas Kather, and D. Truhn. “Multimodal deep learning for integrating chest radiographs and clinical parameters: a case for transformers.” Radiology, 2023.[URL]
  • G. Müller-Franzes, F. Müller-Franzes, L. Huck, V. Raaff, E. Kemmer, F. Khader, S. Tayebi Arasteh, T. Lemainque, J. Nikolas Kather, S. Nebelung, C. Kuhl, and D. Truhn. “Fibroglandular tissue segmentation in breast MRI using vision transformers: a multi-institutional evaluation.” Scientific Reports, 2023.[URL]
  • S. Tayebi Arasteh, C. David Ríos-Urrego, E. Nöth, A. Maier, S. Hee Yang, J. Rusz, and J. Rafael Orozco-Arroyave. “Federated learning for secure development of AI models for Parkinson's disease detection using speech from different languages.” INTERSPEECH 2023.[URL]
  • G. Müller-Franzes, J. Moritz Niehues, F. Khader, S. Tayebi Arasteh, C. Haarburger, C. Kuhl, T. Wang, T. Han, T. Nolte, S. Nebelung, J. Nikolas Kather, and D. Truhn. “A multimodal comparison of latent denoising diffusion probabilistic models and generative adversarial networks for medical image synthesis.” Scientific Reports, 2023.[URL]
  • F. Khader, J. Nikolas Kather, G. Müller-Franzes, T. Wang, T. Han, S. Tayebi Arasteh, K. Hamesch, K. Bressem, C. Haarburger, J. Stegmaier, C. Kuhl, S. Nebelung, and D. Truhn. “Medical transformer for multimodal survival prediction in intensive care: integration of imaging and non-imaging data.” Scientific Reports, 2023.[URL]
  • F. Khader, G. Müller-Franzes, S. Tayebi Arasteh, T. Han, C. Haarburger, M. Schulze-Hagen, P. Schad, S. Engelhardt, B. Baeßler, S. Foersch, J. Stegmaier, C. Kuhl, S. Nebelung, J. Nikolas Kather, and D. Truhn. “Denoising diffusion probabilistic models for 3D medical image generation.” Scientific Reports, 2023.[URL]
  • F. Khader, G. Müller-Franzes, S. Tayebi Arasteh, T. Han, J. Nikolas Kather, J. Stegmaier, S. Nebelung, and D. Truhn. “Vector-quantized latent flows for medical image synthesis and out-of-distribution detection.” IEEE ISBI 2023.[URL]
  • S. Tayebi Arasteh, P. Isfort, M. Saehn, G. Mueller-Franzes, F. Khader, J. Nikolas Kather, C. Kuhl, S. Nebelung, and D. Truhn. “Collaborative training of medical artificial intelligence models with non-uniform labels.” Scientific Reports, 2023.[URL]
  • G. Müller-Franzes, L. Huck, S. Tayebi Arasteh, F. Khader, T. Han, V. Schulz, E. Dethlefsen, J. Nikolas Kather, S. Nebelung, T. Nolte, C. Kuhl, and D. Truhn. “Using machine learning to reduce the need for contrast agents in breast MRI through synthetic images.” Radiology, 2023.[URL]

2021

  • S. Tayebi Arasteh and A. Kalisz. “Conversion between cubic Bézier curves and Catmull-Rom splines.” SN Computer Science, 2021.[URL]
  • S. Tayebi Arasteh, M. Monajem, V. Christlein, P. Heinrich, A. Nicolaou, H. Naderi Boldaji, M. Lotfinia, and S. Evert. “How will your tweet be received? Predicting the sentiment polarity of tweet replies.” IEEE International Conference on Semantic Computing (ICSC) 2021.[URL]

2020

  • S. Tayebi Arasteh. “An empirical study on text-independent speaker verification based on the GE2E method.” arXiv preprint, 2020.[URL]
Editorial service

Editorial boards

Journal review

260+ reviewed manuscripts. Nature Communications, npj Digital Medicine, npj Artificial Intelligence, npj Precision Oncology, Communications Medicine, Scientific Reports, Scientific Data, Radiology: Artificial Intelligence, Medical Image Analysis, Artificial Intelligence in Medicine, AI Open, Computerized Medical Imaging and Graphics, IEEE Transactions on Medical Imaging, IEEE Journal of Biomedical and Health Informatics, IEEE Transactions on Biomedical Engineering, IEEE Transactions on Information Forensics and Security, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Audio, Speech and Language Processing, European Radiology Experimental, Biomedical Engineering Letters, Archives of Computational Methods in Engineering, BMC Medicine, Journal of Medical Internet Research, ACM Computing Surveys, ACM Transactions on Computing for Healthcare, PLOS Digital Health, British Journal of Radiology, Eurosurveillance, and Respirology.

Grant review

German Research Foundation (DFG)

Conference program committees

  • 2026, 14th IEEE International Conference on Healthcare Informatics, Minneapolis, MN, USA
  • 2026, Baiosphere Medical 2026, Erlangen, Germany

Conference review

MICCAI 2026, NeurIPS 2026, ML4H 2026

Awards
  • 2026, Top Reviewer, NeurIPS
  • 2026, Editor's Recognition Award, with Distinction, Radiology: Artificial Intelligence, RSNA
  • 2025, Reviewer of the Year, European Radiology Experimental, European Society of Radiology
  • 2024, Paper of the Month, RWTH Aachen University, Medical Faculty