5 papers
sebis at CRF Filling 2026: A Two-Stage Local LLM Pipeline for Medical CRF Filling
Katharina Sommer, Tristan Till, Florian Matthes
The extraction of structured clinical information from unstructured EHR notes is a persistent bottleneck in healthcare informatics. While large language models (LLMs) offer high pe…
Atomic Fact-Checking Increases Clinician Trust in Large Language Model Recommendations for Oncology Decision Support: A Randomized Controlled Trial
Lisa C. Adams, Linus Marx, Erik Thiele Orberg +8
Question: Does atomic fact-checking, which decomposes AI treatment recommendations into individually verifiable claims linked to source guideline documents, increase clinician trus…
sebis at ArchEHR-QA 2026: How Much Can You Do Locally? Evaluating Grounded EHR QA on a Single Notebook
Ibrahim Ebrar Yurt, Fabian Karl, Tejaswi Choppa +1
Clinical question answering over electronic health records (EHRs) can help clinicians and patients access relevant medical information more efficiently. However, many recent approa…
LLMs for Legal Subsumption in German Employment Contracts
Oliver Wardas, Florian Matthes
Legal work, characterized by its text-heavy and resource-intensive nature, presents unique challenges and opportunities for NLP research. While data-driven approaches have advanced…
AI-assisted German Employment Contract Review: A Benchmark Dataset
Oliver Wardas, Florian Matthes
Employment contracts are used to agree upon the working conditions between employers and employees all over the world. Understanding and reviewing contracts for void or unfair clau…