Zobrazeno 1 - 10
of 137
pro vyhledávání: '"Daniel, Truhn"'
Autor:
Dyke Ferber, Georg Wölflein, Isabella C. Wiest, Marta Ligero, Srividhya Sainath, Narmin Ghaffari Laleh, Omar S. M. El Nahhas, Gustav Müller-Franzes, Dirk Jäger, Daniel Truhn, Jakob Nikolas Kather
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-12 (2024)
Abstract Medical image classification requires labeled, task-specific datasets which are used to train deep learning networks de novo, or to fine-tune foundation models. However, this process is computationally and technically demanding. In language
Externí odkaz:
https://doaj.org/article/3fa7eb85942f4c83b6c9d066cad3cbe7
Autor:
Tianyu Han, Sven Nebelung, Firas Khader, Tianci Wang, Gustav Müller-Franzes, Christiane Kuhl, Sebastian Försch, Jens Kleesiek, Christoph Haarburger, Keno K. Bressem, Jakob Nikolas Kather, Daniel Truhn
Publikováno v:
npj Digital Medicine, Vol 7, Iss 1, Pp 1-9 (2024)
Abstract 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 study, we demonstrate a concerni
Externí odkaz:
https://doaj.org/article/e0f82984e9bc430d804e6ddc1ed52b3c
Autor:
Isabella Catharina Wiest, Dyke Ferber, Jiefu Zhu, Marko van Treeck, Sonja K. Meyer, Radhika Juglan, Zunamys I. Carrero, Daniel Paech, Jens Kleesiek, Matthias P. Ebert, Daniel Truhn, Jakob Nikolas Kather
Publikováno v:
npj Digital Medicine, Vol 7, Iss 1, Pp 1-9 (2024)
Abstract Most clinical information is encoded as free text, not accessible for quantitative analysis. This study presents an open-source pipeline using the local large language model (LLM) “Llama 2” to extract quantitative information from clinic
Externí odkaz:
https://doaj.org/article/068167ddc4594bca801ff425467312af
Autor:
Felix Busch, Lena Hoffmann, Daniel Truhn, Esteban Ortiz-Prado, Marcus R. Makowski, Keno K. Bressem, Lisa C. Adams, COMFORT Consortium
Publikováno v:
BMC Medical Education, Vol 24, Iss 1, Pp 1-20 (2024)
Abstract Background The successful integration of artificial intelligence (AI) in healthcare depends on the global perspectives of all stakeholders. This study aims to answer the research question: What are the attitudes of medical, dental, and veter
Externí odkaz:
https://doaj.org/article/46b371ccaa4c47fb994f21a2a948d2ea
Autor:
Felix Busch, Jakob Nikolas Kather, Christian Johner, Marina Moser, Daniel Truhn, Lisa C. Adams, Keno K. Bressem
Publikováno v:
npj Digital Medicine, Vol 7, Iss 1, Pp 1-6 (2024)
The European Union’s recently adopted Artificial Intelligence (AI) Act is the first comprehensive legal framework specifically on AI. This is particularly important for the healthcare domain, as other existing harmonisation legislation, such as the
Externí odkaz:
https://doaj.org/article/4fc2c0a4090f4a91abd89e4e1a9deabe
Autor:
Teresa Lemainque, Nicola Pridöhl, Shuo Zhang, Marc Huppertz, Manuel Post, Can Yüksel, Masami Yoneyama, Andreas Prescher, Christiane Kuhl, Daniel Truhn, Sven Nebelung
Publikováno v:
European Radiology Experimental, Vol 8, Iss 1, Pp 1-12 (2024)
Abstract Background Quantitative techniques such as T2 and T1ρ mapping allow evaluating the cartilage and meniscus. We evaluated multi-interleaved X-prepared turbo-spin echo with intuitive relaxometry (MIXTURE) sequences with turbo spin-echo (TSE) c
Externí odkaz:
https://doaj.org/article/59f5edc89d244d629337e31d02191578
Autor:
Tianyu Han, Laura Žigutytė, Luisa Huck, Marc Sebastian Huppertz, Robert Siepmann, Yossi Gandelsman, Christian Blüthgen, Firas Khader, Christiane Kuhl, Sven Nebelung, Jakob Nikolas Kather, Daniel Truhn
Publikováno v:
Cell Reports Medicine, Vol 5, Iss 9, Pp 101713- (2024)
Summary: Reliably detecting potentially misleading patterns in automated diagnostic assistance systems, such as those powered by artificial intelligence (AI), is crucial for instilling user trust and ensuring reliability. Current techniques fall shor
Externí odkaz:
https://doaj.org/article/30d6b4c960ef486e997201c823bc662b
Autor:
Suzan Elmaagacli, Christoph Thiele, Franziska Meister, Philipp Menne, Daniel Truhn, Steven W. M. Olde Damink, Johannes Bickenbach, Ulf Neumann, Sven Arke Lang, Florian Vondran, Iakovos Amygdalos
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024)
Abstract Colorectal liver metastases (CRLM) are the predominant factor limiting survival in patients with colorectal cancer and liver resection with complete tumor removal is the best treatment option for these patients. This study examines the predi
Externí odkaz:
https://doaj.org/article/2a89fe375ca540c5893316f515fd248a
Autor:
Gustav Müller-Franzes, Luisa Huck, Maike Bode, Sven Nebelung, Christiane Kuhl, Daniel Truhn, Teresa Lemainque
Publikováno v:
European Radiology Experimental, Vol 8, Iss 1, Pp 1-13 (2024)
Abstract Background To compare denoising diffusion probabilistic models (DDPM) and generative adversarial networks (GAN) for recovering contrast-enhanced breast magnetic resonance imaging (MRI) subtraction images from virtual low-dose subtraction ima
Externí odkaz:
https://doaj.org/article/0a9f02dfb94e457f85041a2a31b2dede
Publikováno v:
Communications Medicine, Vol 4, Iss 1, Pp 1-10 (2024)
Abstract Background The field of Artificial Intelligence (AI) holds transformative potential in medicine. However, the lack of universal reporting guidelines poses challenges in ensuring the validity and reproducibility of published research studies
Externí odkaz:
https://doaj.org/article/8be477c88b9d4523b485e3b9ee70475b