Publications

BibTex format

@article{Micocci:2021:10.3390/jcm10143101,
author = {Micocci, M and Borsci, S and Thakerar, V and Walne, S and Manshadi, Y and Edridge, F and Mullarkey, D and Buckle, P and Hanna, GB},
doi = {10.3390/jcm10143101},
journal = {Journal of Clinical Medicine},
pages = {1--11},
title = {Attitudes towards trusting artificial intelligence insights and factors to prevent the passive adherence of GPs: a pilot study},
url = {http://dx.doi.org/10.3390/jcm10143101},
volume = {10},
year = {2021}
}

Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Artificial Intelligence (AI) systems could improve system efficiency by supporting clinicians in making appropriate referrals. However, they are imperfect by nature and misdiagnoses, if not correctly identified, can have consequences for patient care. In this paper, findings from an online survey are presented to understand the aptitude of GPs (n = 50) in appropriately trusting or not trusting the output of a fictitious AI-based decision support tool when assessing skin lesions, and to identify which individual characteristics could make GPs less prone to adhere to erroneous diagnostics results. The findings suggest that, when the AI was correct, the GPs’ ability to correctly diagnose a skin lesion significantly improved after receiving correct AI information, from 73.6% to 86.8% (X2 (1, N = 50) = 21.787, p < 0.001), with significant effects for both the benign (X2 (1, N = 50) = 21, p < 0.001) and malignant cases (X2 (1, N = 50) = 4.654, p = 0.031). However, when the AI provided erroneous information, only 10% of the GPs were able to correctly disagree with the indication of the AI in terms of diagnosis (d-AIW M: 0.12, SD: 0.37), and only 14% of participants were able to correctly decide the management plan despite the AI insights (d-AIW M:0.12, SD: 0.32). The analysis of the difference between groups in terms of individual characteristics suggested that GPs with domain knowledge in dermatology were better at rejecting the wrong insights from AI. View Full-Text
AU  - Micocci,M
AU  - Borsci,S
AU  - Thakerar,V
AU  - Walne,S
AU  - Manshadi,Y
AU  - Edridge,F
AU  - Mullarkey,D
AU  - Buckle,P
AU  - Hanna,GB
DO  - 10.3390/jcm10143101
EP  - 11
PY  - 2021///
SN  - 2077-0383
SP  - 1
TI  - Attitudes towards trusting artificial intelligence insights and factors to prevent the passive adherence of GPs: a pilot study
T2  - Journal of Clinical Medicine
UR  - http://dx.doi.org/10.3390/jcm10143101
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000676241600001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://www.mdpi.com/2077-0383/10/14/3101/htm#
UR  - http://hdl.handle.net/10044/1/93654
VL  - 10
ER  -

Download

DrSimoneBorsci

Contact

Location

Summary

Citation

BibTex format

RIS format (EndNote, RefMan)