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GPT-4, together with retrieval-augmented generation, can precisely extract and compare medical guideline recommendations in oncology from different associations. Read the Case Study by Dyke Ferber et al.: nejm.ai/3WSVQ19 #ArtificialIntelligence #AIinMedicine

Case Study by Dyke Ferber et al.: GPT-4 for Information Retrieval and Comparison of Medical Oncology Guidelines nejm.ai/3WSVQ19 #ArtificialIntelligence #AIinMedicine

The concept of prompting large language models is not something I had ever heard of but is a fascinating concept. So much to learn and ponder about this field which will shape the next phase of the human story! #EASLCongress

Research in the era of AI session EASLnews chaired by Carolin V. Schneider, MD and Jakob Nikolas Kather with amazing talks by Isabella Wiest, Raquel Pérez-López and Stefano Caruso. Amazing talks, and highly relevant discussion. AI literacy is essential, as is scientific methodology / critical thinking

New preprint from katherlab, led by Isabella Wiest - large language models can solve one of the hardest problems in medical informatics - robustly anonymizing unstructured medical documents. This unlocks a large source of clinical data for downstream AI applications. Available

New preprint from katherlab: large language models for end-to-end clincial trial matching. Our system parses study protocols and patient records, and matches them. We evaluate thoroughly against human experts and find >90% correspondence. Led by Dyke Ferber 🤩

Happy to share our most recent work from katherlab

New research from katherlab, led by Jan Clusmann : "Prompt Injection Attacks on Large Language Models in Oncology" arxiv.org/abs/2407.18981 We show that vision-language models in oncology can be attacked easily, creating malicious output which could harm patients. TU Dresden

In the era of large language models: "The extensive efforts previously required to standardize and make coding systems and ontologies interoperable for traditional computing are no longer necessary." nature.com/articles/s4159… by Jakob Nikolas Kather Daniel Truhn Dyke Ferber Isabella Wiest

This week, we hosted another summer school of medical AI at TU Dresden @Medizin_TUD - led by Narmin Ghaffari Laleh 🤩 with dozens of participants from research teams in 🇮🇹🇫🇷🇷🇴🇩🇪🇺🇸 & plenty of discussions, memes & new research ideas 🌟 Funded by 🇪🇺 via tangerine-ai.eu

Exciting news! 🚀 We've just launched our innovative solution to one of the biggest #challenges in medical research: extracting critical information from free-text data using our #LLM-based pipeline! Jakob Nikolas Kather Dyke Ferber @prof_ebert Keno Bressem Heiko Böhme medrxiv.org/content/10.110…

Check out our newest publication led by Isabella Wiest

New research from katherlab led by Isabella Wiest 🤩

Today, we hosted the first conference on LLMs in medicine at katherlab / TU Dresden, chaired by Isabella Wiest 🤩 We are not just riding the hype train🚆 - we are working hard to provide scientific & clinical evidence for benefits and limitations of LLMs in healthcare

Happy to share a new review article led by Laura Laura Žigutytė from @Katherlab. We surveyed all contributions at Europe’s largest conference on hepatology, EASLnews 2024. The liver research field is already integrating AI techniques for diagnostics, evaluating treatment

In iNNOVATIONS in iGIE, Wiest et al participate in "Deep sight: enhancing periprocedural adverse event recording in endoscopy by structuring text documentation with privacy-preserving large language models." igiejournal.org/article/S2949-… Isabella Wiest Dyke Ferber Jakob Nikolas Kather Chris Thompson, MD

In iNNOVATIONS in iGIE, Wiest et al participate in "Deep sight: enhancing periprocedural adverse event recording in endoscopy by structuring text documentation with privacy-preserving large language models." igiejournal.org/article/S2949-… #GITwitter Isabella Wiest Jakob Nikolas Kather

Happy to share our latest paper in NEJM AI , led by Isabella Wiest : "Deidentifying Medical Documents with Local, Privacy-Preserving Large Language Models: The LLM-Anonymizer" ai.nejm.org/doi/full/10.10… You can use our open source tool with local LLMs to robustly de-identify medical