Abstract:

The integration of machine learning, particularly large language models (LLMs), into medical applications offers great potential to conduct clinical documentation.

This study explores the feasibility and effectiveness of generating structured medical letters exclusively from conversational data between physicians and patients. Using only local models such as the whisper speech-to-text models for transcription and local instance of phi-4 for summarization, we aim to automate the creation of clinical documentation while also generating free to use gold standard datasets for future research. The methodology involves recording 100 real-world physician-patient consultations in clinical settings, transcribing the conversations into text, and generating clinical letters using only local models. These outputs will be systematically evaluated by medical professionals for completeness, accuracy, and clarity against manually created letters. All data processing is conducted securely within the University Hospital Bonn’s infrastructure, ensuring compliance with GDPR and ethical standards. This project provides a novel framework for assessing the practical application of Al in clinical documentation, with implications for improving efficiency in healthcare workflows.

Bio:

Fabian Lechner is a researcher at the Institute for Artificial Intelligence in Medicine and the Institute of Digital Medicine at Philipps University of Marburg. He holds degrees in Business Administration from Aachen and Business Informatics from Marburg. His master’s thesis with Prof. Flek focused on integrating large language models, such as ChatGPT and GPT-3, into medical processes. Since October 2022, Lechner has contributed to several publications, including studies on Al-driven decision support systems in oncology and the adoption of digital health applications among physicians.