Experiments demonstrate transcription methods for clinical trial documents, indicating substantial time savings.
Introduction: Entering the content of manually filled documents in the database of an electronic clinical Trial Master File (eTMF) is a tedious and time-consuming task. Methods: We report experiments for automatic transcription with an optical document recognition pipeline and a web-based (global) and a local multimodal large language model (LLM). Results: Different approaches are best suited for different column types of the table-based documents. Conclusions: Drastic time savings compared to manual transcription are possible.
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Kohlhepp et al. (2025) studied this question.
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