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
Medical image foundation models can predict clinical phenotypes from computed tomography (CT), but strong performance leaves open whether they read disease-specific findings or shortcuts that correlate with the diagnosis. We tested this in 221 electronic-health-record (EHR) phenotypes using Auditable CT phenotyping (AC...
Riga Wu, W. Witschey, Yi-Cheng Li et al.· 0 citations
Purpose: To develop and evaluate an open-weight large language model (LLM) pipeline that converts an entire archive of free-text radiology reports into structured reports without human oversight. Materials and Methods: In this retrospective study, a pipeline with 150 hierarchically organized templates was developed at...
Friedrich Puttkammer, F. Drexel, Marlene Fritzsche et al.· 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
Abstract Background Large language models (LLMs) exhibit extensive medical knowledge but are prone to hallucinations and show low fact-level explainability, limiting clinical adoption and regulatory compliance. Existing approaches, such as retrieval-augmented generation, partially address these issues by grounding answ...
J. Vladika, A. Domres, Mai Q. Nguyen et al.· Journal of Medical Internet...· 0 citations
RadPRISM makes a clinician-defined radiology schema a designated stratification axis: an on-premise large language model extracts per-concept text spans from free-text reports, and each clinical concept is aligned in its own dedicated visual subspace, turning concept stratification into direct, top-level alignment supe...
Fabian Drexel, Marlene Fritzsche, Era Stambollxhiu et al.· 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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