Lay individuals' perceptions of artificial intelligence (<scp>AI</scp>)‐empowered healthcare systems

Zhang, Zhan; Genc, Yegin; Xing, Aiwen; Wang, Dakuo; Fan, Xiangmin; Citardi, Daniel

doi:10.1002/pra2.326

Cited by 17 publications

(6 citation statements)

References 18 publications

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“…We focused here, however, on the public or patient perceptions of AI in the health care context. Overall, the studies on public or patient perceptions report a strong confidence in and acceptability of AI system health care use in the diagnostic context [24][25][26][27][28][29][30].…”

Section: Previous Empirical Researchmentioning

confidence: 97%

Population Preferences for Performance and Explainability of Artificial Intelligence in Health Care: Choice-Based Conjoint Survey

Ploug¹,

Sundby²,

Moeslund³

et al. 2021

J Med Internet Res

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Background Certain types of artificial intelligence (AI), that is, deep learning models, can outperform health care professionals in particular domains. Such models hold considerable promise for improved diagnostics, treatment, and prevention, as well as more cost-efficient health care. They are, however, opaque in the sense that their exact reasoning cannot be fully explicated. Different stakeholders have emphasized the importance of the transparency/explainability of AI decision making. Transparency/explainability may come at the cost of performance. There is need for a public policy regulating the use of AI in health care that balances the societal interests in high performance as well as in transparency/explainability. A public policy should consider the wider public’s interests in such features of AI. Objective This study elicited the public’s preferences for the performance and explainability of AI decision making in health care and determined whether these preferences depend on respondent characteristics, including trust in health and technology and fears and hopes regarding AI. Methods We conducted a choice-based conjoint survey of public preferences for attributes of AI decision making in health care in a representative sample of the adult Danish population. Initial focus group interviews yielded 6 attributes playing a role in the respondents’ views on the use of AI decision support in health care: (1) type of AI decision, (2) level of explanation, (3) performance/accuracy, (4) responsibility for the final decision, (5) possibility of discrimination, and (6) severity of the disease to which the AI is applied. In total, 100 unique choice sets were developed using fractional factorial design. In a 12-task survey, respondents were asked about their preference for AI system use in hospitals in relation to 3 different scenarios. Results Of the 1678 potential respondents, 1027 (61.2%) participated. The respondents consider the physician having the final responsibility for treatment decisions the most important attribute, with 46.8% of the total weight of attributes, followed by explainability of the decision (27.3%) and whether the system has been tested for discrimination (14.8%). Other factors, such as gender, age, level of education, whether respondents live rurally or in towns, respondents’ trust in health and technology, and respondents’ fears and hopes regarding AI, do not play a significant role in the majority of cases. Conclusions The 3 factors that are most important to the public are, in descending order of importance, (1) that physicians are ultimately responsible for diagnostics and treatment planning, (2) that the AI decision support is explainable, and (3) that the AI system has been tested for discrimination. Public policy on AI system use in health care should give priority to such AI system use and ensure that patients are provided with information.

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Section: Previous Empirical Researchmentioning

confidence: 97%

Population Preferences for Performance and Explainability of Artificial Intelligence in Health Care: Choice-Based Conjoint Survey

Ploug¹,

Sundby²,

Moeslund³

et al. 2021

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…26,27,33 Despite its high potential, little is known about patients' perceptions and acceptance of this novel technology. 34,35 Our study set out to fill this knowledge gap by examining patients' perceptions of using AI-based health systems in the context of comprehending radiology reports received via the patient portal. This is part of a larger research effort to design and develop AI-powered informatics tools to aid patients interpreting their diagnostic results (e.g.…”

Section: Introductionmentioning

confidence: 99%

Patients’ perceptions of using artificial intelligence (AI)-based technology to comprehend radiology imaging data

Zhang¹,

Citardi

Wang³

et al. 2021

Health Informatics J

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Results of radiology imaging studies are not typically comprehensible to patients. With the advances in artificial intelligence (AI) technology in recent years, it is expected that AI technology can aid patients’ understanding of radiology imaging data. The aim of this study is to understand patients’ perceptions and acceptance of using AI technology to interpret their radiology reports. We conducted semi-structured interviews with 13 participants to elicit reflections pertaining to the use of AI technology in radiology report interpretation. A thematic analysis approach was employed to analyze the interview data. Participants have a generally positive attitude toward using AI-based systems to comprehend their radiology reports. AI is perceived to be particularly useful in seeking actionable information, confirming the doctor’s opinions, and preparing for the consultation. However, we also found various concerns related to the use of AI in this context, such as cyber-security, accuracy, and lack of empathy. Our results highlight the necessity of providing AI explanations to promote people’s trust and acceptance of AI. Designers of patient-centered AI systems should employ user-centered design approaches to address patients’ concerns. Such systems should also be designed to promote trust and deliver concerning health results in an empathetic manner to optimize the user experience.

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“…Identifying and addressing these concerns (Hanhui, 2019) is crucial for the acceptance of AI by its users (medical staff and patients) (Zhang et al, 2020). Previous studies on medical personnel suggest that not everyone is willing to accept the use of medical AI devices (Laï, Brian, & Mamzer, 2020;Turja, Aaltonen, Taipale, & Oksanen, 2020).…”

Section: Literature Review and Theoretical Modelmentioning

confidence: 99%

Social acceptance of artificial intelligence (AI) application for improving medical service diagnostics

Ejdys,

Czerwińska,

Ginevičius

2024

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The aim of the conducted research was to assess the attitude of the Polish society towards the use of artificial intelligence in medical diagnostics. In the research process, we sought answers to three research questions: how trust in the use of AI for medical diagnostics can be measured; if societal openness to technology determines trust in the use of AI for medical diagnostics purposes; and if a higher level of trust in the use of AI for medical diagnostics influences the potential improvement in the quality of medical diagnostics as perceived by Poles. The authors' particular focus was on the following three constructs and the relationships between them: openness to new technologies (OP), willingness to trust AI in medical diagnostics (T), and perceived impact of AI application on the quality of medical diagnostic services (PI). A survey was conducted on a representative sample of 1063 Polish respondents to seek answers to the above questions. The survey was conducted using the CATI technique.

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Lay individuals' perceptions of artificial intelligence (AI)‐empowered healthcare systems

Cited by 17 publications

References 18 publications

Population Preferences for Performance and Explainability of Artificial Intelligence in Health Care: Choice-Based Conjoint Survey

Population Preferences for Performance and Explainability of Artificial Intelligence in Health Care: Choice-Based Conjoint Survey

Patients’ perceptions of using artificial intelligence (AI)-based technology to comprehend radiology imaging data

Social acceptance of artificial intelligence (AI) application for improving medical service diagnostics

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