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This paper presents medBERTde, a pre-trained German BERT model specificallydesigned for the German medical domain. The model has been trained on a largecorpus of 4.7 Million German medical documents and has been shown to achievenew state-of-the-art performance on eight different medical benchmarks coveringa wide range of disciplines and medical document types. In addition toevaluating the overall performance of the model, this paper also conducts amore in-depth analysis of its capabilities. We investigate the impact of datadeduplication on the model's performance, as well as the potential benefits ofusing more efficient tokenization methods. Our results indicate thatdomain-specific models such as medBERTde are particularly useful for longertexts, and that deduplication of training data does not necessarily lead toimproved performance. Furthermore, we found that efficient tokenization playsonly a minor role in improving model performance, and attribute most of theimproved performance to the large amount of training data. To encourage furtherresearch, the pre-trained model weights and new benchmarks based onradiological data are made publicly available for use by the scientificcommunity.

MEDBERT.de: A Comprehensive German BERT Model for the Medical Domain