This bachelor’s thesis examines the integration of large language models into medical questionnaires using the example of a checklist from the Deutsche Krebsgesellschaft for recording hereditary risk factors for breast and ovarian cancer. The aim is to develop and evaluate an LLM-assisted user interface that supports users in answering complex questions, thereby improving usability and user experience. As part of a human-centered design process, a proof of concept was designed, developed, and tested in a qualitative study with six participants. The user interface includes arti- ficial intelligence support from two LLM agents that communicate with each other and generate appropriate outputs for users. The results of the study show improved trans- parency, guidance, and efficiency through LLM assistance, while reducing the overall complexity of the questionnaire. The work confirms the potential of large language models to support medical survey in- struments, but also provides an outlook on necessary improvements and future research areas for the further development of such applications.
Tim Jakob Lamich (Wed,) studied this question.
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