Imperial College London
Portrait Picture

Professor Aldo Faisal

Faculty of Engineering, Department of Bioengineering

Professor of AI & Neuroscience
 
 
 
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Contact

 

+44 (0)20 7594 6373a.faisal Website

 
 
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Assistant

 

Miss Teresa Ng +44 (0)20 7594 8300

 
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Location

 

4.08Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Nagendran:2023:10.1038/s41746-023-00955-z,
author = {Nagendran, M and Festor, P and Komorowski, M and Gordon, A and Faisal, A},
doi = {10.1038/s41746-023-00955-z},
journal = {npj Digital Medicine},
title = {Quantifying the impact of AI recommendations with explanations on prescription decision making},
url = {http://dx.doi.org/10.1038/s41746-023-00955-z},
volume = {6},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - The influence of AI recommendations on physician behaviour remains poorly characterised. We assess how clinicians' decisions may be influenced by additional information more broadly, and how this influence can be modified by either the source of the information (human peers or AI) and the presence or absence of an AI explanation (XAI, here using simple feature importance). We used a modified between-subjects design where intensive care doctors (N=86) were presented on a computer for each of 16 trials with a patient case and prompted to prescribe continuous values for two drugs. We used a multi-factorial experimental design with four arms, where each clinician experienced all four arms on different subsets of our 24 patients. The four arms were (i) baseline (control), (ii) peer human clinician scenario showing what doses had been prescribed by other doctors, (iii) AI suggestion and (iv) XAI suggestion. We found that additional information (peer, AI or XAI) had a strong influence on prescriptions (significantly for AI, not so for peers) but simple XAI did not have higher influence than AI alone. There was no correlation between attitudes to AI or clinical experience on the AI-supported decisions and nor was there correlation between what doctors self-reported about how useful they found the XAI and whether the XAI actually influenced their prescriptions. Our findings suggest that the marginal impact of simple XAI was low in this setting and we also cast doubt on the utility of self-reports as a valid metric for assessing XAI in clinical experts.
AU  - Nagendran,M
AU  - Festor,P
AU  - Komorowski,M
AU  - Gordon,A
AU  - Faisal,A
DO  - 10.1038/s41746-023-00955-z
PY  - 2023///
SN  - 2398-6352
TI  - Quantifying the impact of AI recommendations with explanations on prescription decision making
T2  - npj Digital Medicine
UR  - http://dx.doi.org/10.1038/s41746-023-00955-z
UR  - http://hdl.handle.net/10044/1/107632
VL  - 6
ER  -
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