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Quantifying the impact of AI recommendations with explanations on prescription decision making

Medicine and Health

Quantifying the impact of AI recommendations with explanations on prescription decision making

M. Nagendran, P. Festor, et al.

This study by Myura Nagendran, Paul Festor, Matthieu Komorowski, Anthony C. Gordon, and Aldo A. Faisal delves into the intriguing effects of AI recommendations on physician prescription choices in the ICU. With 86 participants, the research reveals AI significantly sways decisions, yet simple explanations do not enhance this influence, challenging existing notions in the clinical domain.

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Playback language: English
Abstract
This study investigates how AI recommendations, with or without explanations (XAI), influence physician prescription decisions in critical care. 86 ICU doctors participated in a modified between-subjects design, evaluating four arms: baseline, peer recommendations, AI suggestions, and AI suggestions with XAI (simple feature importance). Results showed a strong influence of AI (but not peer) recommendations on prescriptions, while simple XAI had minimal additional impact. Clinician attitudes towards AI and experience did not correlate with AI-influenced decisions, nor did self-reported XAI usefulness correlate with its influence on prescriptions. The findings highlight the limited marginal impact of simple XAI in this context and question the reliability of self-reported data as a metric for assessing XAI in clinical settings.
Publisher
npj Digital Medicine
Published On
Nov 16, 2023
Authors
Myura Nagendran, Paul Festor, Matthieu Komorowski, Anthony C. Gordon, Aldo A. Faisal
Tags
AI recommendations
critical care
physician prescription
explainable AI
decision-making
ICU
clinical settings
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