Attitudes towards Trusting Artificial Intelligence Insights and Factors to Prevent the Passive Adherence of GPs: A Pilot Study
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 ar...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Journal of Clinical Medicine |
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| Online Access: | https://www.mdpi.com/2077-0383/10/14/3101 |
| _version_ | 1797526853782601728 |
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| author | Massimo Micocci Simone Borsci Viral Thakerar Simon Walne Yasmine Manshadi Finlay Edridge Daniel Mullarkey Peter Buckle George B. Hanna |
| author_facet | Massimo Micocci Simone Borsci Viral Thakerar Simon Walne Yasmine Manshadi Finlay Edridge Daniel Mullarkey Peter Buckle George B. Hanna |
| author_sort | Massimo Micocci |
| collection | DOAJ |
| description | 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 (<i>n</i> = 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% (X<sup>2</sup> (1, <i>N</i> = 50) = 21.787, <i>p</i> < 0.001), with significant effects for both the benign (X<sup>2</sup> (1, <i>N</i> = 50) = 21, <i>p</i> < 0.001) and malignant cases (X<sup>2</sup> (1, <i>N</i> = 50) = 4.654, <i>p</i> = 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. |
| first_indexed | 2024-03-10T09:35:15Z |
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| institution | Directory Open Access Journal |
| issn | 2077-0383 |
| language | English |
| last_indexed | 2024-03-10T09:35:15Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Clinical Medicine |
| spelling | doaj.art-5208b3a0227f408fabb86831a41bc6a02023-11-22T04:06:43ZengMDPI AGJournal of Clinical Medicine2077-03832021-07-011014310110.3390/jcm10143101Attitudes towards Trusting Artificial Intelligence Insights and Factors to Prevent the Passive Adherence of GPs: A Pilot StudyMassimo Micocci0Simone Borsci1Viral Thakerar2Simon Walne3Yasmine Manshadi4Finlay Edridge5Daniel Mullarkey6Peter Buckle7George B. Hanna8NIHR London In-Vitro Diagnostics Cooperative, London W2 1PE, UKNIHR London In-Vitro Diagnostics Cooperative, London W2 1PE, UKDepartment of Primary Care and Public Health, Imperial College London, London W6 8RP, UKNIHR London In-Vitro Diagnostics Cooperative, London W2 1PE, UKSkin Analytics Limited, London EC2A 4PS, UKSkin Analytics Limited, London EC2A 4PS, UKSkin Analytics Limited, London EC2A 4PS, UKNIHR London In-Vitro Diagnostics Cooperative, London W2 1PE, UKNIHR London In-Vitro Diagnostics Cooperative, London W2 1PE, UKArtificial 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 (<i>n</i> = 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% (X<sup>2</sup> (1, <i>N</i> = 50) = 21.787, <i>p</i> < 0.001), with significant effects for both the benign (X<sup>2</sup> (1, <i>N</i> = 50) = 21, <i>p</i> < 0.001) and malignant cases (X<sup>2</sup> (1, <i>N</i> = 50) = 4.654, <i>p</i> = 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.https://www.mdpi.com/2077-0383/10/14/3101artificial intelligencetrustpassive adherencehuman factors |
| spellingShingle | Massimo Micocci Simone Borsci Viral Thakerar Simon Walne Yasmine Manshadi Finlay Edridge Daniel Mullarkey Peter Buckle George B. Hanna Attitudes towards Trusting Artificial Intelligence Insights and Factors to Prevent the Passive Adherence of GPs: A Pilot Study Journal of Clinical Medicine artificial intelligence trust passive adherence human factors |
| title | Attitudes towards Trusting Artificial Intelligence Insights and Factors to Prevent the Passive Adherence of GPs: A Pilot Study |
| title_full | Attitudes towards Trusting Artificial Intelligence Insights and Factors to Prevent the Passive Adherence of GPs: A Pilot Study |
| title_fullStr | Attitudes towards Trusting Artificial Intelligence Insights and Factors to Prevent the Passive Adherence of GPs: A Pilot Study |
| title_full_unstemmed | Attitudes towards Trusting Artificial Intelligence Insights and Factors to Prevent the Passive Adherence of GPs: A Pilot Study |
| title_short | Attitudes towards Trusting Artificial Intelligence Insights and Factors to Prevent the Passive Adherence of GPs: A Pilot Study |
| title_sort | attitudes towards trusting artificial intelligence insights and factors to prevent the passive adherence of gps a pilot study |
| topic | artificial intelligence trust passive adherence human factors |
| url | https://www.mdpi.com/2077-0383/10/14/3101 |
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