Jakob Nikolas Kather

2 weeks ago | biorxiv.org | Verena Bitto |Xiaofeng Jiang |Michael Baumann |Jakob Nikolas Kather

AbstractComputational pathology-based models are becoming increasingly popular for extracting biomarkers from images of cancer tissue. However, their validity is often only demonstrated on a single unseen validation cohort, limiting insights into their generalizability and posing challenges for explainability. In this study, we developed models to predict overall survival using haematoxylin and eosin (H&E) slides from FFPE samples in head and neck squamous cell carcinoma (HNSCC).

Dec 5, 2024 | fis.tu-dresden.de | Charlotte Syrykh |Jakob Nikolas Kather |Camille Laurent |Université de Toulouse

The advent of digital pathology and the deployment of high-throughput molecular techniques are generating an unprecedented mass of data. Thanks to advances in computational sciences, artificial intelligence (AI) approaches represent a promising avenue for extracting relevant information from complex data structures.

Oct 22, 2024 | onlinelibrary.wiley.com | Charlotte Syrykh |Jakob Nikolas Kather |TUD Dresden |Camille Laurent

AI artificial intelligence AUCROC area under the receiver operating characteristic curve CLL chronic lymphocytic leukemia/lymphocytic lymphoma COO Cell Of Origin DLBCL diffuse large B-cell lymphoma FISH fluorescence in situ hybridization FL follicular lymphoma H&E haematoxylin-eosin IPI international prognostic index MIPI-b biologic-Mantle cell lymphoma international prognostic index ML machine learning NLP natural language processing WSI whole slide image Introduction Lymphomas are among the...

Sep 5, 2024 | nature.com | Jakob Nikolas Kather

The development of clinically relevant artificial intelligence (AI) models has traditionally required access to extensive labelled datasets, which inevitably centre AI advances around large centres and private corporations. Data availability has also dictated the development of AI applications: most studies focus on common cancer types, and leave rare diseases behind.

Oct 10, 2023 | nature.com | Fiona Kolbinger |Hannah Sophie Muti |Zunamys I. Carrero |Jan-Niklas Eckardt |Narmin Ghaffari Laleh |Michaela Unger | +6 more

Large language models (LLMs) are artificial intelligence (AI) tools specifically trained to process and generate text. LLMs attracted substantial public attention after OpenAI’s ChatGPT was made publicly available in November 2022. LLMs can often answer questions, summarize, paraphrase and translate text on a level that is nearly indistinguishable from human capabilities. The possibility to actively interact with models like ChatGPT makes LLMs attractive tools in various fields, including medicine. While these models have the potential to democratize medical knowledge and facilitate access to healthcare, they could equally distribute misinformation and exacerbate scientific misconduct due to a lack of accountability and transparency. In this article, we provide a systematic and comprehensive overview of the potentials and limitations of LLMs in clinical practice, medical research and medical education. Clusmann et al. describe how large language models such as ChatGPT could be used in medical practice, research and education. These models could democratize medical knowledge and facilitate access to healthcare, but there are also potential limitations to be considered.