Keno K Bressem
Overview
Explore the profile of Keno K Bressem including associated specialties, affiliations and a list of published articles.
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58
Citations
410
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Recent Articles
1.
Adams L, Bressem K
Radiol Artif Intell
. 2025 Mar;
7(2):e250016.
PMID: 40042400
No abstract available.
2.
Kim S, Schramm S, Adams L, Braren R, Bressem K, Keicher M, et al.
NPJ Digit Med
. 2025 Feb;
8(1):97.
PMID: 39934372
Recent advancements in large language models (LLMs) have created new ways to support radiological diagnostics. While both open-source and proprietary LLMs can address privacy concerns through local or cloud deployment,...
3.
Graf M, Bressem K, Adams L
Radiologie (Heidelb)
. 2025 Feb;
PMID: 39934245
Background: The rapid development of large language models (LLMs) opens up new possibilities for the automated processing of medical texts. Transforming unstructured radiology reports into structured data is crucial for...
4.
Busch F, Hoffmann L, Rueger C, van Dijk E, Kader R, Ortiz-Prado E, et al.
Commun Med (Lond)
. 2025 Jan;
5(1):26.
PMID: 39838160
Background: The introduction of large language models (LLMs) into clinical practice promises to improve patient education and empowerment, thereby personalizing medical care and broadening access to medical knowledge. Despite the...
5.
Dorfner F, Vahldiek J, Donle L, Zhukov A, Xu L, Hantze H, et al.
RMD Open
. 2024 Dec;
10(4.
PMID: 39719299
Purpose: To examine whether incorporating anatomy-centred deep learning can improve generalisability and enable prediction of disease progression. Methods: This retrospective multicentre study included conventional pelvic radiographs of four different patient...
6.
Fast D, Adams L, Busch F, Fallon C, Huppertz M, Siepmann R, et al.
NPJ Digit Med
. 2024 Dec;
7(1):358.
PMID: 39668168
Autonomous Medical Evaluation for Guideline Adherence (AMEGA) is a comprehensive benchmark designed to evaluate large language models' adherence to medical guidelines across 20 diagnostic scenarios spanning 13 specialties. It includes...
7.
Busch F, Prucker P, Komenda A, Ziegelmayer S, Makowski M, Bressem K, et al.
Eur J Radiol
. 2024 Nov;
182:111827.
PMID: 39566177
Purpose: Large language models (LLMs) promise to streamline radiology reporting. With the release of OpenAI's GPT-4o (Generative Pre-trained Transformers-4 omni), which processes not only text but also speech, multimodal LLMs...
8.
Dorfner F, Jurgensen L, Donle L, Al Mohamad F, Bodenmann T, Cleveland M, et al.
Radiology
. 2024 Oct;
313(1):e241139.
PMID: 39470431
Background Rapid advances in large language models (LLMs) have led to the development of numerous commercial and open-source models. While recent publications have explored OpenAI's GPT-4 to extract information of...
9.
Han T, Nebelung S, Khader F, Wang T, Muller-Franzes G, Kuhl C, et al.
NPJ Digit Med
. 2024 Oct;
7(1):288.
PMID: 39443664
Large language models (LLMs) have broad medical knowledge and can reason about medical information across many domains, holding promising potential for diverse medical applications in the near future. In this...
10.
Busch F, Hoffmann L, Pinto Dos Santos D, Makowski M, Saba L, Prucker P, et al.
Eur Radiol
. 2024 Oct;
PMID: 39438330
Structured reporting (SR) has long been a goal in radiology to standardize and improve the quality of radiology reports. Despite evidence that SR reduces errors, enhances comprehensiveness, and increases adherence...