28 citations · 64 across the 12 of their papers we have counts for
13 papers
The Value of System 1 Models in Medicine
Xiufeng Yang, Konstantinos Vrettos, Zeineb Ben Chaaben +17
Many clinical decisions are a choice among a few fixed options, taken quickly and in large numbers. Such decisions also need an uncertainty estimate to know when to defer to a clin…
RadPRISM: Schema-stratified radiology-report supervision for concept-disentangled image representations and visual grounding
Fabian Drexel, Marlene Fritzsche, Era Stambollxhiu +15
Vision-language pretraining learns rich medical image representations from radiology reports, but previous model variants commonly operate within a single shared embedding space, s…
Medical foundation models converge less under label supervision
Soroosh Tayebi Arasteh, Sebastian Ziegelmayer, Mahshad Lotfinia +4
Diagnostic classifiers and imaging biomarkers are fitted on the embeddings of medical foundation models. These models are replaced as new versions appear. This practice assumes tha…
Information-seeking failures of large language models in agentic clinical reasoning
Krischan Braitsch, Laura K. Schmalbrock, Theresa Weltermann +11
Large language models achieve high scores on medical knowledge assessments, yet clinical reasoning requires actively deciding what to investigate under uncertainty. We developed an…
Vision-language models for chest radiography do not always need the image
Mahshad Lotfinia, Sebastian Ziegelmayer, Lisa Adams +4
Vision-language models that answer questions about chest radiographs are evaluated by their accuracy on labels derived from radiology reports. High benchmark accuracy is often inte…
Routine laboratory trajectories encode the onset of organ-level complications in cancer
Jannik Lübberstedt, Krischan Braitsch, Jacqueline Lammert +21
Routine laboratory panels drawn during cancer treatment constitute longitudinal physiological recordings of organ function, yet their temporal structure is discarded by single-time…
Benchmarking Foundation Models for Renal Lesion Stratification in CT
Hartmut Häntze, Sarah de Boer, Myrthe Buser +7
The rapid proliferation of open-source medical foundation models (FMs) raises a practical question: how well do their pre-trained representations transfer to clinically relevant bu…
Atomic Fact-Checking Increases Clinician Trust in Large Language Model Recommendations for Oncology Decision Support: A Randomized Controlled Trial
Lisa C. Adams, Linus Marx, Erik Thiele Orberg +8
Question: Does atomic fact-checking, which decomposes AI treatment recommendations into individually verifiable claims linked to source guideline documents, increase clinician trus…
Agentic clinical reasoning over longitudinal myeloma records: a retrospective evaluation against expert consensus
Johannes Moll, Jannik Lübberstedt, Christoph Nuernbergk +21
Multiple myeloma is managed through sequential lines of therapy over years to decades, with each decision depending on cumulative disease history distributed across dozens to hundr…
PARROT: An Open Multilingual Radiology Reports Dataset
Bastien Le Guellec, Kokou Adambounou, Lisa C Adams +85
Rationale and Objectives: To develop and validate PARROT (Polyglottal Annotated Radiology Reports for Open Testing), a large, multicentric, open-access dataset of fictional radiolo…
MRSegmentator: Multi-Modality Segmentation of 40 Classes in MRI and CT
Hartmut Häntze, Lina Xu, Christian J. Mertens +29
Purpose: To develop and evaluate a deep learning model for multi-organ segmentation of MRI scans. Materials and Methods: The model was trained on 1,200 manually annotated 3D axial…
Evaluation of GPT-4 for chest X-ray impression generation: A reader study on performance and perception
Sebastian Ziegelmayer, Alexander W. Marka, Nicolas Lenhart +7
The remarkable generative capabilities of multimodal foundation models are currently being explored for a variety of applications. Generating radiological impressions is a challeng…
Efficient, high-performance pancreatic segmentation using multi-scale feature extraction
Moritz Knolle, Georgios Kaissis, Friederike Jungmann +5
For artificial intelligence-based image analysis methods to reach clinical applicability, the development of high-performance algorithms is crucial. For example, existent segmentat…