Clinicians' Voice: Fundamental Considerations for XAI in Healthcare

TL;DR

This paper explores clinicians' perspectives on explainable AI in healthcare, emphasizing the importance of user input, workflow integration, and training for successful adoption of AI tools.

Contribution

It provides a holistic, exploratory analysis of clinicians' needs and concerns, highlighting requirements for effective XAI implementation in healthcare.

Findings

Clinicians are generally positive about AI in healthcare.

Workflow integration and clinician-patient relations are key concerns.

Training is crucial for effective AI adoption.

Abstract

Explainable AI (XAI) holds the promise of advancing the implementation and adoption of AI-based tools in practice, especially in high-stakes environments like healthcare. However, most of the current research lacks input from end users, and therefore their practical value is limited. To address this, we conducted semi-structured interviews with clinicians to discuss their thoughts, hopes, and concerns. Clinicians from our sample generally think positively about developing AI-based tools for clinical practice, but they have concerns about how these will fit into their workflow and how it will impact clinician-patient relations. We further identify training of clinicians on AI as a crucial factor for the success of AI in healthcare and highlight aspects clinicians are looking for in (X)AI-based tools. In contrast to other studies, we take on a holistic and exploratory perspective to identify general requirements for (X)AI products for healthcare before moving on to testing specific tools.