Extending the Visual Data Exploration Loop towards Trustworthy Machine Learning in the Healthcare Domain

Integration of machine learning (ML) systems into healthcare settings creates novel opportunities, including pattern recognition in heterogeneous medical datasets, clinical decision support as well as processes automation to save time, advance the quality of care, reduce costs and relieve healthcare staff. Challenges include opaque digital systems, curbed autonomy as well as require- ments on communication, interaction and human-machine decision-making. Obstacles involve the interprofessional gap between data scientists and healthcare professionals (HCPs) during model development as well as the lack of trust into ML models. Visual Analytics (VA) enables versatile interactions between users and ML models via adaptable visualizations and has been success- fully deployed to improve accuracy, identify bias and increase trust. However, specifically supporting HCPs to gain trust into ML models through VA systems is not sufficiently explored. We propose an extended visual data exploration framework towards trustworthy ML in the healthcare domain for multidisciplinary teams of data scientists, VA experts and HCPs. Additionally, we apply our framework to three real-world use cases for policy development, plausibility testing and model optimization.