TY - JOUR AU - Heilmeyer, Felix AU - Böhringer, Daniel AU - Reinhard, Thomas AU - Arens, Sebastian AU - Lyssenko, Lisa AU - Haverkamp, Christian PY - 2024 DA - 2024/8/28 TI - Viability of Open Large Language Models for Clinical Documentation in German Health Care: Real-World Model Evaluation Study JO - JMIR Med Inform SP - e59617 VL - 12 KW - machine learning KW - ML KW - artificial intelligence KW - AI KW - large language model KW - large language models KW - LLM KW - LLMs KW - natural language processing KW - NLP KW - deep learning KW - algorithm KW - algorithms KW - model KW - models KW - analytics KW - practical model KW - practical models KW - medical documentation KW - writing assistance KW - medical administration KW - writing assistance for physicians AB - Background: The use of large language models (LLMs) as writing assistance for medical professionals is a promising approach to reduce the time required for documentation, but there may be practical, ethical, and legal challenges in many jurisdictions complicating the use of the most powerful commercial LLM solutions. Objective: In this study, we assessed the feasibility of using nonproprietary LLMs of the GPT variety as writing assistance for medical professionals in an on-premise setting with restricted compute resources, generating German medical text. Methods: We trained four 7-billion–parameter models with 3 different architectures for our task and evaluated their performance using a powerful commercial LLM, namely Anthropic’s Claude-v2, as a rater. Based on this, we selected the best-performing model and evaluated its practical usability with 2 independent human raters on real-world data. Results: In the automated evaluation with Claude-v2, BLOOM-CLP-German, a model trained from scratch on the German text, achieved the best results. In the manual evaluation by human experts, 95 (93.1%) of the 102 reports generated by that model were evaluated as usable as is or with only minor changes by both human raters. Conclusions: The results show that even with restricted compute resources, it is possible to generate medical texts that are suitable for documentation in routine clinical practice. However, the target language should be considered in the model selection when processing non-English text. SN - 2291-9694 UR - https://medinform.jmir.org/2024/1/e59617 UR - https://doi.org/10.2196/59617 DO - 10.2196/59617 ID - info:doi/10.2196/59617 ER -