Large language models (LLMs) can automate the conversion of radiologic free text into structured reports, but multidisciplinary reader preferences for the resulting output remain insufficiently studied. This prospective, two-phase, fixed-sequence within-participant study evaluated reader assessments of LLM-structured v...
Felix Busch, A. Marka, Lilly F. Wingberg et al.· Cancer Imaging· 0 citations
RATIONALE AND OBJECTIVES
To evaluate the impact of four commercially available AI solutions for chest radiography on diagnostic performance, workflow efficiency, and clinical decision-making in a real-world setting.
MATERIALS AND METHODS
In this prospective, monocentric, crossover reader study, five readers (one to s...
T. Lemke, A. Marka, P. Prucker et al.· Academic Radiology· 0 citations
Large language models achieve high scores on medical knowledge assessments, yet clinical reasoning requires actively deciding what to investigate under uncertainty. We developed an agentic evaluation framework in hematologic oncology in which models must proactively request clinical data across three sequential rounds...
K. Braitsch, L. Schmalbrock, Theresa Weltermann et al.· arXiv.org· 1 citation
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