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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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Hauptverfasser: Ferber, Dyke (VerfasserIn) , El Nahhas, Omar S. M. (VerfasserIn) , Wölflein, Georg (VerfasserIn) , Wiest, Isabella (VerfasserIn) , Clusmann, Jan (VerfasserIn) , Leßmann, Marie-Elisabeth (VerfasserIn) , Foersch, Sebastian (VerfasserIn) , Lammert, Jacqueline (VerfasserIn) , Tschochohei, Maximilian (VerfasserIn) , Jäger, Dirk (VerfasserIn) , Salto-Tellez, Manuel (VerfasserIn) , Schultz, Nikolaus (VerfasserIn) , Truhn, Daniel (VerfasserIn) , Kather, Jakob Nikolas (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: 06 June 2025
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
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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
Beschreibung
Zusammenfassung: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.
Beschreibung:Online veröffentlicht: 06. Juni 2025
Gesehen am 24.09.2025
Beschreibung:Online Resource
ISSN:2662-1347
DOI:10.1038/s43018-025-00991-6

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