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Articles by Jan Clusmann
On-premise medical AI agents for reliable clinical decision-making
Abstract Autonomous clinical artificial intelligence (AI) agents powered by large language models (LLMs), meaning systems that can complete a diagnostic workflow without continuous human input, are increasingly capable of supporting complex reasoning and decision-making. Clinical translation, however, remains limited by two unmet requirements: institutionally governed deployment and reliable decision-time uncertainty estimation.
Safety and security of large language models in healthcare
Integration of artificial intelligence methods into clinical care is proceeding rapidly, driven by advances in generative artificial intelligence, most notably large language models. Large language models trained on large amounts of text have shown potential across nearly every domain of healthcare. However, their broad applicability also comes with new responsibilities, vulnerabilities and threats. These need to be assessed and mitigated before widespread clinical adoption.
Safety and security of large language models in healthcare
Abstract Integration of artificial intelligence methods into clinical care is proceeding rapidly, driven by advances in generative artificial intelligence, most notably large language models. Large language models trained on large amounts of text have shown potential across nearly every domain of healthcare. However, their broad applicability also comes with new responsibilities, vulnerabilities and threats. These need to be assessed and mitigated before widespread clinical adoption.
Machine learning for population-level risk prediction of future cholangiocarcinoma
Keywords Predictive modelling Machine learning Big data Cholangiocarcinoma Risk stratification Evidence before this study Cholangiocarcinoma (CCA) remains a rare and highly lethal malignancy, often diagnosed at advanced stages due to its asymptomatic progression, rendering curative surgical resection less feasible.
A deep learning framework for efficient pathology image analysis
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Abstract Artificial intelligence has transformed digital pathology by enabling biomarker prediction from high-resolution whole-slide images. However, current methods are computationally inefficient, processing thousands of redundant tiles per slide and requiring complex aggregation models. We introduce EAGLE (Efficient Approach for Guided Local Examination), a deep learning framework that emulates pathologists by selectively analyzing informative regions.
By Peter Neidlinger, Tim Lenz, Sebastian Foersch, Jan Clusmann, Marco Gustav, Lawrence A Shaktah, Rupert Langer, Bastian Dislich, Lisa Boardman, Amy French, Ellen L. Goode, Andrea Gsur, Stefanie Brezina, Marc J. Gunter, Robert Steinfelder, Christoph Röcken, Tabitha A. Harrison, Ulrike Peters, Amanda I Phipps, Giuseppe Curigliano, Nicola Fusco, Antonio Marra, Michael Hoffmeister, Hermann Brenner, Jakob Nikolas Kather, Chiara M. L. Loeffler, Hans-Michael Behrens
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Nature
Verified
Towards autonomous medical artificial intelligence agents
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Abstract Large language models (LLMs) show great potential for clinical decision-making, yet most applications remain narrow, task-specific chat tools rather than systems integrated into clinical workflows1,2. However, building physician copilots will require models that operate within the electronic health record (EHR), with governed access to patient data and the ability to initiate permitted EHR actions within defined safety constraints.
By Dyke Ferber, Lars Hilgers, Christiane Höper, Benedict Kinny-Köster, Jan-Niklas Eckardt, Katharina Egger-Heidrich, Marius Bill, Jan Clusmann, Lejla Kadric, Marcel Oehme, Maximilian Mayrhofer-Schmid, Alexander Oeser, Georg Wölflein, Isabella C. Wiest, Jan Moritz Middeke, A. John Iafrate, Daniel Truhn, Dirk Jäger, Jakob Nikolas Kather
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Nature
Verified
Spatial biomarker discovery via interpretable semantic learning in histopathology
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ArticleVolume 44, Issue 8p1605-1622.e7Open access Affiliations & Notes 1Else Kroener Fresenius Center for Digital Health, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany 2State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China 3Department of Thoracic Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center,...
By Junhao Liang, Xiaofeng Jiang, Nic Gabriel Reitsam, Tim Lenz, Li Zhang, Marco Gustav, Zunamys Itzell Carrero, Hannah Sophie Muti, Peter Neidlinger, Jan Clusmann, Laura Žigutytė, Vidhya Sainath, Fabian Wolf, Lisa Boardman, Amy French, Ellen L. Goode, Andrea Gsur, Stefanie Brezina, Marc J. Gunter, Robert Steinfelder, Tabitha A. Harrison, Ulrike Peters, Amanda I Phipps, Philip Quirke, Nicholas West, Michael Hoffmeister, Hermann Brenner, Durgesh Wankhede, Jitendra Jonnagaddala, Nicholas Hawkins, Robyn Ward, Elena Fountzilas, Kyriaki Papadopoulou, George Fountzilas, Thierry Andre, Thierry André, Julien Taieb, Jean-François Emile, Magali Svrcek, Lingjie Kong, Jakob Nikolas Kather
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Cell Press
Verified
Spatial biomarker discovery via interpretable semantic learning in histopathology
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ArticleVolume 44, Issue 8p1605-1622.e7Open access Affiliations & Notes 1Else Kroener Fresenius Center for Digital Health, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany 2State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China 3Department of Thoracic Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center,...
By Junhao Liang, Xiaofeng Jiang, Nic Gabriel Reitsam, Tim Lenz, Li Zhang, Marco Gustav, Zunamys Itzell Carrero, Hannah Sophie Muti, Peter Neidlinger, Jan Clusmann, Laura Žigutytė, Vidhya Sainath, Fabian Wolf, Lisa Boardman, Amy French, Ellen L. Goode, Andrea Gsur, Stefanie Brezina, Marc J. Gunter, Robert Steinfelder, Tabitha A. Harrison, Ulrike Peters, Amanda I Phipps, Philip Quirke, Nicholas West, Michael Hoffmeister, Hermann Brenner, Durgesh Wankhede, Jitendra Jonnagaddala, Nicholas Hawkins, Robyn Ward, Elena Fountzilas, Kyriaki Papadopoulou, George Fountzilas, Thierry Andre, Thierry André, Julien Taieb, Jean-François Emile, Magali Svrcek, Lingjie Kong, Jakob Nikolas Kather
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Cell Press
Verified
Forschungsportal der TU Dresden
Hepatocellular carcinoma (HCC) is a highly fatal tumor, for which risk stratification is crucial, yet remains challenging. Here, we develop an interpretable machine-learning framework for HCC risk stratification based on routinely collected clinical data. We utilize prospectively collected multimodal data from over 900,000 individuals and 983 cases of HCC across two population-scale cohorts: the "UK Biobank study" (development) and the "All of Us Research Program" (external testing).
Research Portal of the TU Dresden
Clusmann, J., Balaguer-Montero, M., Bassegoda, O., Schneider, C. V., Seraphin, T., Paintsil, E. & Luedde, T. & 9 others , Lopez, R. P., Calderaro, J., Gilbert, S., Marjot, T., Spann, A., Shawcross, D. L., Lens, S., Trépo, E. & Kather, J. N. , 18 Jul 2025 , (E-pub ahead of print) In: Journal of hepatology. 83 , 6 , p. 1410-1426 17 p. Research output: Contribution to journal › Review article › Contributed › peer-review
Large language models for clinical decision support in gastroenterology and hepatology - Nature Reviews Gastroenterology & Hepatology
Abstract Clinical decision making in gastroenterology and hepatology has become increasingly complex and challenging for physicians. This growing complexity can be addressed by computational tools that support clinical decisions. Although numerous clinical decision support systems (CDSS) have emerged, they have faced difficulties with real-world performance and generalizability, resulting in limited clinical adoption.
Large language models for clinical decision support in gastroenterology and hepatology
Clinical decision making in gastroenterology and hepatology has become increasingly complex and challenging for physicians. This growing complexity can be addressed by computational tools that support clinical decisions. Although numerous clinical decision support systems (CDSS) have emerged, they have faced difficulties with real-world performance and generalizability, resulting in limited clinical adoption.
Large language models for clinical decision support in gastroenterology and hepatology
Does Castor Oil Really Help You Debloat? Here’s what experts say. Bloating can be super uncomfortable, so some people are willing to try pretty much anything to find relief. One internet-favorite way to debloat is naval pulling, a practice rooted in an ancient Ayurvedic tradition, Melinda Ring, MD, director of the Osher Center for …
Prompt injection attacks on vision language models in oncology
Abstract External IDs PubMed 39890777
Comorbidities, mortality and metabolic profile in individuals with primary biliary cholangitis-A Phenome‐Wide‐Association‐Study
Here, we provide insights into novel and established associations of primary biliary cholangitis, an immune-mediated cholestatic liver disease, with a variety of other diseases. Moreover, we describe alterations in serum lipidomics and metabolomics, which could represent a link between the pathophysiological processes underlying PBC development, progression and associated morbidity.
The future landscape of large language models in medicine - Communications Medicine
Model outputs were generated using ChatGPT (model versions: GPT-4—May 3, 2023, GPT-3.5—May 3, 2023). Additional example outputs are listed in the Supplementary Data. Clinical recommendation Administration of prokinetic agents can be fatal in patients with (mechanical) ileus. This false recommendation was output by GPT-3.5 but could not be reproduced with GPT-4. Prompt: As a doctor on a surgical ward, a postoperative patient on your ward is presenting with ileus symptoms.
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