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
TAP-JEPA is introduced, a ViT-B/3D trained on 113,611 CT volumes that is competitive with state-of-the-art encoders in scan-level classification and shows that contextual predictability is not a sufficient proxy for clinical relevance in medical representation learning.
F. Drexel, B. Kainz, J. Kather et al.· 0 citations
Background/Objectives: To assess associations between quantitative computed tomography (CT) features, inflammatory markers, and length of hospital stay (LOS) in acute pyelonephritis (APN). Methods: This retrospective single-center study included 82 patients with CT-confirmed APN. Two radiologists quantified renal perfu...
Markus M. Graf, T. Lemke, A. Marka et al.· Diagnostics· 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
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
A narrative synthesis of the human-AI interaction and radiology AI literature highlighted three underrecognized determinants of successful human-AI collaboration in radiology, and concrete research directions are proposed to bridge the gap between algorithmic capabilities and clinical utility.
Su Hwan Kim, L. Adams, B. Wiestler et al.· Radiology: Artificial Intell...· 0 citations
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