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Author

L. Adams

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Open access Sep 2026

Correlation of CT-Derived Quantitative Image Features and Inflammatory Laboratory Markers with Length of Hospital Stay in Patients with Pyelonephritis

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. · 0 citations
Preprint Aug 2026

Auditable CT Phenotyping Through Report-derived Radiological Observations

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
Review Open access Aug 2026

Artificial Intelligence-Assisted Chest Radiography: A Prospective Crossover Multi-Reader Study on Diagnostic Performance and Workflow Efficiency.

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. · 0 citations
Open access Sep 2026

Improving Reliability and Explainability of Medical Question Answering Through Atomic Fact-Checking in Retrieval-Augmented Large Language Models: Creation and Validation Study

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. · 0 citations
Preprint Jul 2026

RadPRISM: Schema-stratified radiology-report supervision for concept-disentangled image representations and visual grounding

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
#generative ai Editorial Sep 2026

Human-AI Collaboration in Radiology: The Blind Spots.

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. · 0 citations

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