Which explanations do clinicians prefer? A comparative evaluation of XAI understandability and actionability in predicting the need for hospitalization

Which explanations do clinicians prefer? A comparative evaluation of XAI understandability and actionability in predicting the need for hospitalization

Abstract Background This study aims to address the gap in understanding clinicians’ attitudes toward explainable AI (XAI) methods applied to machine learning models using tabular data, commonly found in clinical settings. It specifically explores clinicians’ perceptions of different XAI methods from...

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Main Authors: Laura Bergomi, Giovanna Nicora, Marta Anna Orlowska, Chiara Podrecca, Riccardo Bellazzi, Caterina Fregosi, Francesco Salinaro, Marco Bonzano, Giuseppe Crescenzi, Francesco Speciale, Santi Di Pietro, Valentina Zuccaro, Erika Asperges, Paolo Sacchi, Pietro Valsecchi, Elisabetta Pagani, Michele Catalano, Chandra Bortolotto, Lorenzo Preda, Enea Parimbelli
Format: Article
Language:English
Published: BMC 2025-07-01
Series:BMC Medical Informatics and Decision Making
Subjects:
User study
Questionnaire
Human-AI collaboration
Interpretability
Think-aloud protocol
Clinical decision-making
Online Access:https://doi.org/10.1186/s12911-025-03045-0
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https://doi.org/10.1186/s12911-025-03045-0

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