Related Experiment Video
Updated: May 27, 2026

TBase - an Integrated Electronic Health Record and Research Database for Kidney Transplant Recipients
Published on: April 13, 2021
Investigating expectations and needs regarding the use of large language models at Bavarian university clinics
Juraj Vladika1, Alexander Fichtl2, Florian Matthes2
1Department of Computer Science, TUM School of Computation, Information, and Technology, Technical University of Munich, Garching, Germany. juraj.vladika@tum.de.
Abstract:
Recent advancements in Artificial Intelligence (AI) have been driven by Large Language Models (LLMs), powerful tools capable of generating coherent text and solving diverse analytical tasks. While LLMs hold great potential to enhance healthcare by assisting physicians and improving patient treatment, their clinical adoption is limited, and there is a lack of statistically grounded information on the opinions of medical professionals, personnel, and students regarding LLM usage. To address this gap, we conducted an online survey from April to October 2024, gathering insights from 120 participants across five Bavarian university clinics (in Germany), including physicians, medical students, and administrative staff. Findings show that many participants already use LLMs for research support, summarization, translation, and report drafting. Most believe LLMs will positively influence their field, acknowledge their potential to automate mundane tasks, and believe they will help to achieve a more personalized, evidence-based, and cost-effective patient treatment. However, concerns were shown regarding their opaque nature, data privacy, and the potential loss of patient trust. Participants overwhelmingly feel their institutions are not well-prepared for LLM adoption, with suggestions for improvement including increased education and specialized training, investments in digitalization and infrastructure, ensuring legal compliance, and encouraging technological openness. We hope these insights will inform the design of future medical AI solutions.
More Related Videos
03:14Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
Published on: December 6, 2024
05:47Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025