An Exploration of Visual Analytic Techniques for XAI: Applications in Clinical Decision Support
Abstract
Artificial Intelligence (AI) systems exhibit considerable potential in providing decision support across various domains. In this context, the methodology of eXplainable AI (XAI) becomes crucial, as it aims to enhance the transparency and comprehensibility of AI models' decision-making processes. However, after a review of XAI methods and their application in clinical decision support, there exist notable gaps within the XAI methodology, particularly concerning the effective communication of explanations to users.
This thesis aims to bridge these existing gaps by presenting in Chapter 3 a framework designed to communicate AI-generated explanations effectively to end-users. This is particularly pertinent in fields like healthcare, where the successful implementation of AI decision support hinges on the ability to convey actionable insights to medical professionals.
Building upon this framework, subsequent chapters illustrate how visualization and visual analytics can be used with XAI in the context of clinical decision support. Chapter 4 introduces a visual analytic tool designed for ranking and triaging patients in the intensive care unit (ICU). Leveraging various XAI methods, the tool enables healthcare professionals to understand how the ranking model functions and how individual patients are prioritized. Through interactivity, users can explore influencing factors, evaluate alternate scenarios, and make informed decisions for optimal patient care.
The pivotal role of transparency and comprehensibility within machine learning models is explored in Chapter 5. Leveraging the power of explainable AI techniques and visualization, it investigates the factors contributing to model performance and errors. Furthermore, it investigates scenarios in which the model outperforms, ultimately fostering user trust by shedding light on the model's strengths and capabilities.
Recognizing the ethical concerns associated with predictive models in health, Chapter 6 addresses potential bias and discrimination in ranking systems. By using the proposed visual analytic tool, users can assess the fairness and equity of the system, promoting equal treatment. This research emphasizes the need for unbiased decision-making in healthcare.
Having developed the framework and illustrated ways of combining XAI with visual analytics in the service of clinical decision support, the thesis concludes by identifying important future directions of research in this area.