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http://dx.doi.org/10.1097/WCO.0000000000001417 | DOI Listing |
Curr Opin Neurol
October 2025
Friedrich-Baur-Institute, Department of Neurology, LMU Clinic, Munich, Germany.
medRxiv
August 2025
The Windreich Department of Artificial Intelligence and Human Health, Mount Sinai Medical Center, NY, USA.
Background: AI agents built on large language models (LLMs) can plan tasks, use external tools, and coordinate with other agents. Unlike standard LLMs, agents can execute multi-step processes, access real-time clinical information, and integrate multiple data sources. There has been interest in using such agents for clinical and administrative tasks, however, there is limited knowledge on their performance and whether multi-agent systems function better than a single agent for healthcare tasks.
View Article and Find Full Text PDFFront Artif Intell
August 2025
Bayer Research and Development, Pharmaceuticals, Preclinical Development, Berlin, Germany.
The pharmaceutical industry faces pressure to improve the drug development process while reducing costs in an evolving regulatory landscape. This paper presents the Preclinical Information Center (PRINCE), a cloud-hosted data integration platform developed by Bayer AG in collaboration with Thoughtworks. PRINCE integrates decades of structured and unstructured safety study reports, leveraging a multi-agent architecture based on Large Language Models (LLMs) and advanced data retrieval methodologies, such as Retrieval-Augmented Generation and Text-to-SQL.
View Article and Find Full Text PDFKorean J Radiol
September 2025
Department of Radiology, Artificial Intelligence Laboratory, Mayo Clinic, Rochester, MN, USA.
Int J Mol Sci
August 2025
Moffitt Cancer Center, Tampa, FL 33612, USA.
Artificial intelligence (AI) and its machine learning and deep learning algorithms have shown promise in oncological practice. Spatial information analysis in the context of cancer is crucial for its diagnosis and treatment because it can provide an understanding of tumor-microenvironment interactions and reveal insights into response to treatment. AI tools can analyze spatial information at multiple scales, highlighting key disease, clinical, and genetic phenotypes that may reveal underlying mechanisms and molecular markers of response and resistance within the tumor and its microenvironment.
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