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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| Hauptverfasser: | , , , , , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
06 June 2025
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| In: |
Nature cancer
Year: 2025, Jahrgang: 6, Heft: 8, Pages: 1337-1349 |
| ISSN: | 2662-1347 |
| DOI: | 10.1038/s43018-025-00991-6 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1038/s43018-025-00991-6 Verlag, kostenfrei, Volltext: https://www.nature.com/articles/s43018-025-00991-6 |
| Verfasserangaben: | 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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| 245 | 1 | 0 | |a Development and validation of an autonomous artificial intelligence agent for clinical decision-making in oncology |c 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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| 520 | |a 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. | ||
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