Impact of AI Decision Support on Clinical Experts' Radiographic Interpretation of Adamantinomatous Craniopharyngioma.

Abstract

This research explores the integration of Artificial Intelligence (AI) into clinical decision-making in pediatric brain tumor care, specifically Adamantinomatous Craniopharyngioma (ACP). We present a user-centered design approach to introducing AI tools into clinical workflows to support decision-making in managing Central Nervous System tumors. We conducted a controlled experiment with six clinical experts to explore the hypothesis that AI integrated into clinical contexts can improve the radiographic interpretation of ACP. We found that AI assistance reduced task difficulty and enhanced clinical efficiency; we also discovered variations in user behavior during the annotation process. We identified multiple challenges, including the interpretive complexity of radiographic images and increased disagreements among clinicians when AI was employed. Our study underscores the importance of a nuanced understanding of clinician experiences for successful AI integration into a high-stakes clinical workflow.

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