We present a multimodal dialogue system that allows doctors to interact with a medical decision support system in virtual reality (VR). We integrate an interactive visualization of patient records and radiology image data, as well as therapy predictions. Therapy predictions are computed in real-time using a deep learning model.

We describe a corpus of multimodal dialogues with an MP3player collected in Wizard-of-Oz experiments and annotated with a richfeature set at several layers. We are using the Nite XML Toolkit (NXT) to represent and further process the data. We designed an NXTdata model, converted experiment log file data and manualtranscriptions into NXT, and are building tools for additionalannotation using NXT libraries. The annotated corpus will be used to (i) investigate various aspects of multimodal presentation andinteraction strategies both within and across annotation layers; (ii) design an initial policy for reinforcement learning of multimodalclarification requests.