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Development and validation of an autonomous artificial intelligence agent for clinical decision-making in oncology

Clinical decision-making in oncology is complex, requiring the integration of multimodal data and multidomain expertise. We developed and evaluated an autonomous clinical artificial intelligence (AI) agent leveraging GPT-4 with multimodal precision oncology tools to support personalized clinical dec...

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Main Authors: Ferber, Dyke (Author) , El Nahhas, Omar S. M. (Author) , Wölflein, Georg (Author) , Wiest, Isabella (Author) , Clusmann, Jan (Author) , Leßmann, Marie-Elisabeth (Author) , Foersch, Sebastian (Author) , Lammert, Jacqueline (Author) , Tschochohei, Maximilian (Author) , Jäger, Dirk (Author) , Salto-Tellez, Manuel (Author) , Schultz, Nikolaus (Author) , Truhn, Daniel (Author) , Kather, Jakob Nikolas (Author)
Format: Article (Journal)
Language:English
Published: 06 June 2025
In: Nature cancer
Year: 2025, Volume: 6, Issue: 8, Pages: 1337-1349
ISSN:2662-1347
DOI:10.1038/s43018-025-00991-6
Online Access:Verlag, kostenfrei, Volltext: https://doi.org/10.1038/s43018-025-00991-6
Verlag, kostenfrei, Volltext: https://www.nature.com/articles/s43018-025-00991-6
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Author Notes:Dyke Ferber, Omar S.M. El Nahhas, Georg Wölflein, Isabella C. Wiest, Jan Clusmann, Marie-Elisabeth Leßmann, Sebastian Foersch, Jacqueline Lammert, Maximilian Tschochohei, Dirk Jäger, Manuel Salto-Tellez, Nikolaus Schultz, Daniel Truhn & Jakob Nikolas Kather
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Summary:Clinical decision-making in oncology is complex, requiring the integration of multimodal data and multidomain expertise. We developed and evaluated an autonomous clinical artificial intelligence (AI) agent leveraging GPT-4 with multimodal precision oncology tools to support personalized clinical decision-making. The system incorporates vision transformers for detecting microsatellite instability and KRAS and BRAF mutations from histopathology slides, MedSAM for radiological image segmentation and web-based search tools such as OncoKB, PubMed and Google. Evaluated on 20 realistic multimodal patient cases, the AI agent autonomously used appropriate tools with 87.5% accuracy, reached correct clinical conclusions in 91.0% of cases and accurately cited relevant oncology guidelines 75.5% of the time. Compared to GPT-4 alone, the integrated AI agent drastically improved decision-making accuracy from 30.3% to 87.2%. These findings demonstrate that integrating language models with precision oncology and search tools substantially enhances clinical accuracy, establishing a robust foundation for deploying AI-driven personalized oncology support systems.
Item Description:Online veröffentlicht: 06. Juni 2025
Gesehen am 24.09.2025
Physical Description:Online Resource
ISSN:2662-1347
DOI:10.1038/s43018-025-00991-6

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