A User Study Evaluating Argumentative Explanations in Diagnostic Decision Support

TL;DR

This study evaluates how different AI-generated explanations impact physicians' trust and understanding in diagnostic decision support, aiming to identify the most effective explanation types for clinical use.

Contribution

It presents a user study with physicians assessing various explanation types, explicitly evaluating their effectiveness in diagnostic decision support systems.

Findings

Certain explanation types significantly improved trust and understanding.

Physicians preferred explanations that were transparent and aligned with clinical reasoning.

Qualitative insights highlight the importance of context-specific explanations.

Abstract

As the field of healthcare increasingly adopts artificial intelligence, it becomes important to understand which types of explanations increase transparency and empower users to develop confidence and trust in the predictions made by machine learning (ML) systems. In shared decision-making scenarios where doctors cooperate with ML systems to reach an appropriate decision, establishing mutual trust is crucial. In this paper, we explore different approaches to generating explanations in eXplainable AI (XAI) and make their underlying arguments explicit so that they can be evaluated by medical experts. In particular, we present the findings of a user study conducted with physicians to investigate their perceptions of various types of AI-generated explanations in the context of diagnostic decision support. The study aims to identify the most effective and useful explanations that enhance the diagnostic process. In the study, medical doctors filled out a survey to assess different types of explanations. Further, an interview was carried out post-survey to gain qualitative insights on the requirements of explanations incorporated in diagnostic decision support. Overall, the insights gained from this study contribute to understanding the types of explanations that are most effective.