Trust at every step: Embedding trust quality gates into the visual data exploration loop for machine learning-based clinical decision support systems

Recent advancements in machine learning (ML) support novel applications in healthcare, most significantly clinical decision support systems (CDSS). The lack of trust hinders acceptance and is one of the main reasons for the limited number of successful implementations in clinical practice. Visual analytics enables the development of trustworthy ML models by providing versatile interactions and visualizations for both data scientists and healthcare professionals (HCPs). However, specific support for HCPs to build trust towards ML models through visual analytics remains underexplored. We propose an extended visual data exploration methodology to enhance trust in ML-based healthcare applications. Based on a literature review on trustworthiness of CDSS, we analyze emerging themes and their implications. By introducing trust quality gates mapped onto the Visual Data Exploration Loop, we provide structured checkpoints for multidisciplinary teams to assess and build trust. We demonstrate the applicability of this methodology in three real-world use cases - policy development, plausibility testing, and model optimization - highlighting its potential to advance trustworthy ML in the healthcare domain.