eprintid: 10143985 rev_number: 7 eprint_status: archive userid: 699 dir: disk0/10/14/39/85 datestamp: 2022-02-22 10:41:00 lastmod: 2022-02-22 10:41:00 status_changed: 2022-02-22 10:41:00 type: article metadata_visibility: show sword_depositor: 699 creators_name: Jermutus, Eva creators_name: Kneale, Dylan creators_name: Thomas, James creators_name: Michie, Susan title: Influences on User Trust in Healthcare Artificial Intelligence: A Systematic Review ispublished: pub divisions: C07 divisions: F66 divisions: B02 divisions: UCL divisions: D05 divisions: B14 divisions: J81 divisions: B16 keywords: Trust, artificial intelligence, human-AI interaction, health care, healthcare AI, systematic review, MMAT, logic model note: © 2022 Jermutus E et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. abstract: 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 (HAI) applications so far have been underexplored. The objective of this study was to identify aspects related to users, AI applications and the wider context influencing trust in HAI. METHODS: We performed a systematic review to map out influences on user trust in HAI. To identify relevant studies, we searched seven 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. CONCLUSIONS: Trust in HAI depends on a variety of factors, both external and internal to AI applications. 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. date: 2022-02-18 date_type: published publisher: F1000 Research Ltd official_url: https://doi.org/10.12688/wellcomeopenres.17550.1 oa_status: green full_text_type: pub language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 1940077 doi: 10.12688/wellcomeopenres.17550.1 lyricists_name: Michie, Susan lyricists_name: Thomas, James lyricists_name: Kneale, Dylan lyricists_id: SFMIC21 lyricists_id: JTHOA32 lyricists_id: DKNEA72 actors_name: Kneale, Dylan actors_id: DKNEA72 actors_role: owner full_text_status: public publication: Wellcome Open Research volume: 7 article_number: 65 citation: Jermutus, Eva; Kneale, Dylan; Thomas, James; Michie, Susan; (2022) Influences on User Trust in Healthcare Artificial Intelligence: A Systematic Review. Wellcome Open Research , 7 , Article 65. 10.12688/wellcomeopenres.17550.1 <https://doi.org/10.12688/wellcomeopenres.17550.1>. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10143985/1/5bcea361-1fa7-4fbc-b579-b85d7e8a2159_17550_-_eva_jermutus_%281%29.pdf