Medical-Domain-Expert-Centered AI-Assisted Digitized Histopathology Education Tool

Education in the medical domain with the help of digitized content using state-of-the-art technology is a current topic, nowadays. In this paper, we are presenting our ongoing research on streamlining the medical domain expert workflow in the field of histopathology by designing a graphical user interface for an artificial-intelligence-powered (AI-powered) assistance educational tool for digitized specimen annotations. We found out that the approach to the presentation of the AI-powered features in the user interface should be built upon the level of expertise of the domain expert. The differences in interactions with the proposed annotation tools were observed in a case study with five participants with different levels of expertise from ongoing research cooperation with medical faculty students and domain experts in the field of histopathology. We discuss the quantitative and qualitative outcomes from the aforementioned case study to serve as a base for maximizing the benefits leveraging from applying AI-powered assistance tools in the field of education in digitized histopathology.