On-premise medical AI agents for reliable clinical decision-making.

Li Zhang¹, Georg Wölflein¹, Dyke Ferber^1,2, Junhao Liang¹, Zunamys I Carrero¹, Xuewei Wu^2,3, Julien Vibert^2,4,5, Jan Clusmann^1,6, Lino Möhrmann^1,6, Elena E Möhrmann¹, Catharina Wichmann^1,7, Fabian Wolf¹, Tim Lenz¹, Jakob Nikolas Kather^8,9,10,11

¹Else Kroener Fresenius Center for Digital Health, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany.

²Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany.

³Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, China.

⁴Drug Development Department, Gustave Roussy, Villejuif, France.

⁵Université Paris-Saclay, Gustave Roussy, INSERM U981, IHU PRISM, Villejuif, France.

⁶Department of Medicine I, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany.

⁷Department of Gynecology and Obstetrics, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany.

⁸Else Kroener Fresenius Center for Digital Health, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany. kather.jn@tu-dresden.de.

⁹Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany. kather.jn@tu-dresden.de.

¹⁰Department of Medicine I, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany. kather.jn@tu-dresden.de.

¹¹Pathology & Data Analytics, Leeds Institute of Medical Research at St. James's, University of Leeds, Leeds, UK. kather.jn@tu-dresden.de.

PubMed DOI

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…

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