Influences On User Trust In Healthcare Artificial Intelligence (HAI) - A Systematic Review - V2 - Info and Reading Options
By Eva Jermutus
“Influences On User Trust In Healthcare Artificial Intelligence (HAI) - A Systematic Review - V2” Metadata:
- Title: ➤ Influences On User Trust In Healthcare Artificial Intelligence (HAI) - A Systematic Review - V2
- Author: Eva Jermutus
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- Internet Archive ID: osf-registrations-gp8rm-v1
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"Influences On User Trust In Healthcare Artificial Intelligence (HAI) - A Systematic Review - V2" Description:
The Internet Archive:
Background: Artificial Intelligence (AI) is becoming increasingly prominent in domains such as healthcare. It is argued to be transformative through altering the way in which healthcare data is used. The realisation and success of AI depend heavily on people’s trust in its applications. Yet, influences on trust in healthcare AI applications so far have been underexplored. Objective: The objective of this study was to identify aspects related to users, the AI application and the wider context influencing trust in healthcare AI (HAI). Methods: We performed a systematic review to map out influences on user trust in HAI. To identify relevant studies, we searched 7 electronic databases in November 2019 (ACM digital library, IEEE Explore, NHS Evidence, ProQuest Dissertations & Thesis Global, PsycINFO, PubMed, Web of Science Core Collection). Searches were restricted to publications available in English and German. To be included studies had to be empirical; focus on an AI application (excluding robotics) in a health-related setting; and evaluate applications with regards to users. Results: Three studies, one mixed-method and two qualitative studies in English were included. Influences on trust fell into three broad categories: human-related (knowledge, expectation, mental model, self-efficacy, type of user, age, gender), AI-related (data privacy and safety, operational safety, transparency, design, customizability, trialability, explainability, understandability, power-control-balance, benevolence) and context-related (AI company, media, users’ social network). The factors resulted in an updated logic model illustrating the relationship between these aspects. Conclusion: Trust in healthcare AI depends on a variety of factors, both external and internal to the AI application. This study contributes to our understanding of what influences trust in HAI by highlighting key influences as well as pointing to gaps and issues in existing research on trust and AI. In so doing, it offers a starting point for further investigation of trust environments as well as trustworthy AI applications.
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