Automated Classification of Radiation Oncology Safety Events Using Large Language Models: A Novel Approach to Streamline Reporting and Enable Retrospective Analysis.
This is the first study to apply an LLM for automated classification of radiation oncology safety events and to outline a framework for future reporting systems that leverage artificial intelligence (AI)-assisted workflows, demonstrating strong potential for automating retrospective safety event tagging and streamlining future reporting workflows.
We investigated the potential utility of large language models (LLMs) in supporting patient safety efforts. Specifically, we evaluated the reasoning capabilities of LLMs in performing root cause analysis (RCA) of radiation oncology incidents using narrative reports from the Radiation Oncology Incident Learning System (...
Yun-Tao Wang, M. De Ornelas, M. T. Studenski et al.· PLOS Digital Health· 0 citations
The synthesis is shifted from plausible report generation to clinically interpretable effectiveness, safety, and workflow effects, and the evidence remains too heterogeneous, biased, and sparse on case-level end points to support a pooled meta-analysis or autonomous clinical-readiness claims.
Jie-Li Huang, Ji-Qing Zhu, Xiao-Guang Ni· Journal of Medical Internet...· 0 citations
The design and early usability and adoption evaluation of The Daily Dose, an LLM-driven system for automated clinical summarization and trial identification in radiation oncology, was widely adopted and generally favorably perceived.
J. Holmes, F. Mastroleo, M. Borras-Osorio et al.· Clinical and Translational R...· 0 citations
BACKGROUND
Patient safety events (PSEs) are preventable incidents that cause, or have the potential to cause, harm to patients during their medical journey. Although incident reporting systems capture large volumes of such events, only a small proportion undergo comprehensive investigation due to the resource-intensive...
Hong-Bo Chen, Shehnaz Islam, L. Pozzobon et al.· JMIR Medical Informatics· 0 citations
Errors in radiology reports are a major patient-safety concern and are difficult to detect with manual quality assurance (QA). Large language models (LLMs) can assist, but generic prompting does not reflect radiologists’ structured, section-based workflows. To develop and evaluate RadCoT (Radiological Chain-of-Thought)...
Jia Li, Zi-Chun Zhou, Yan-Tao Niu et al.· European Radiology Experimen...· 0 citations
An open-source, human-verified workflow using large language models can accelerate electronic health record abstraction while improving accuracy and supports broader adoption of transparent artificial intelligence methods in clinical research.
Carl Jannes Neuse, Malte Janssen, S. Ibing et al.· BMC Medical Informatics and...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
