176 citations · 257 across the 8 of their papers we have counts for
8 papers
DNA Methylation Profiling in Melanoma: From Lesion Classification to Therapeutic Stratification
Jana T. Winterstein, Lukas Heinlein, Günter Raddatz +34
DNA methylation provides a stable record of cellular identity, capturing epigenetic programs that distinguish specialized cell states despite a shared genome. Because malignant tra…
Large language models improve physician accuracy but lead to false reliance
Tirtha Chanda, Christoph Wies, Franziska Schramm +10
Retrieval-augmented large language models (LLMs) promise source-linked clinical support, but their value depends on whether displayed evidence guides rather than distorts physician…
Bridging Radiology and Pathology: A DICOM-Based Framework for Multimodal Mapping and Integrated Visualization
Nilesh P. Rijhwani, Titus J. Brinker, Peter Neher +4
Accurate disease diagnosis depends on effective collaboration between medical specialties, yet departments often use distinct data systems and proprietary formats. This heterogenei…
Superhuman performance in urology board questions by an explainable large language model enabled for context integration of the European Association of Urology guidelines: the UroBot study
Martin J. Hetz, Nicolas Carl, Sarah Haggenmüller +4
Large Language Models (LLMs) are revolutionizing medical Question-Answering (medQA) through extensive use of medical literature. However, their performance is often hampered by out…
Clinical Melanoma Diagnosis with Artificial Intelligence: Insights from a Prospective Multicenter Study
Lukas Heinlein, Roman C. Maron, Achim Hekler +27
Early detection of melanoma, a potentially lethal type of skin cancer with high prevalence worldwide, improves patient prognosis. In retrospective studies, artificial intelligence…
Evaluating Deep Learning-based Melanoma Classification using Immunohistochemistry and Routine Histology: A Three Center Study
Christoph Wies, Lucas Schneider, Sarah Haggenmueller +6
Pathologists routinely use immunohistochemical (IHC)-stained tissue slides against MelanA in addition to hematoxylin and eosin (H&E)-stained slides to improve their accuracy in dia…
Using Multiple Dermoscopic Photographs of One Lesion Improves Melanoma Classification via Deep Learning: A Prognostic Diagnostic Accuracy Study
Achim Hekler, Roman C. Maron, Sarah Haggenmüller +28
Background: Convolutional neural network (CNN)-based melanoma classifiers face several challenges that limit their usefulness in clinical practice. Objective: To investigate the im…
Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma
Tirtha Chanda, Katja Hauser, Sarah Hobelsberger +32
Although artificial intelligence (AI) systems have been shown to improve the accuracy of initial melanoma diagnosis, the lack of transparency in how these systems identify melanoma…