Table 2.

Characteristics of included studies .

Author(s)	Country of origin	Methodological design	Healthcare setting	Aim of the study	Application area	Intended user	Definition of Trust
Datta Burton et al. (38), 2021	The United Kingdom	Opinion paper, with empirical support	Neurology	To explore questions of trust between patients and clinicians and between clinicians and researchers.	Brain modelling	Clinicians (unspecified)	A triangle of trust; “relationships between patients and clinicians, and between clinicians and researchers” (38).
Choi et al. (39), 2020	The United States & Canada	Opinion paper, without empirical support	Radiology	To outline several ethical and practical concerns in integrating AI with human cognition in the real-world: bias and pitfalls of AI, ethics of trust and risk regarding AI, and design of the human—AI interface.	Image recognition	Clinicians (radiologist)	“A human's propensity to submit to vulnerability and unpredictability, and nevertheless to use that automation, as measured by intention expressed in speech or writing, or by measurable bodily actions to actually use the automation” (40).
Esmaeilzadeh et al. (41), 2021	The United States	Quantitative: survey study	Healthcare, general	To examine how potential users perceive the benefits, risks, and use of AI clinical applications for their healthcare purposes and how their perception may be different if faced with three healthcare service encounter scenarios.	Diagnosis and treatment	Patients (with acute or chronic conditions)	“Trust can be defined as trust in clinicians and the clinical tools they use (such as AI clinical applications)” (42).
Fan et al. (43), 2018	China	Quantitative: survey study	Hospital	To explore the adoption of artificial intelligence-based medical diagnosis support system by integrating Unified theory of user acceptance of technology and trust theory.	Diagnosis	Clinicians (unspecified)	“The beliefs about a technology's capability rather than its will or its motives.” (44).
Liu & Tao, (45), 2022	China	Quantitative: survey study	Healthcare service delivery	To examine the roles of trust and three AI-specific in public acceptance of smart healthcare services based on an extended Technology Acceptance Model.	Smart healthcare services	The general population	“The degree to which an individual perceives that smart healthcare services are dependable, reliable, and trustworthy in supporting one's healthcare activities” (45).
Prakash & Das, (46), 2021	India	Mixed methods	Radiology	To develop and test a model based on theories of Unified Theory of Acceptance and Use of Technology, status quo bias, and technology trust.	Diagnosis	Clinicians (radiologist)	“The willingness of a party to be vulnerable to the actions of another party…” (47).
Roski et al. (48), 2021	The United States	Opinion paper, without empirical support	Healthcare, general	To describe how AI risk mitigation practices could be promulgated through strengthened industry self-governance, specifically through certification and accreditation of AI development and implementation organizations.	AI, general	N/a	N/a
Yakar et al. (49), 2021	Netherlands	Quantitative: survey study	Radiology, dermatology, and robotic surgery	To investigate the general population's view AI in medicine with specific emphasis on three areas that have experienced major progress in AI research in the past years, namely radiology, robotic surgery, and dermatology.	Diagnosis, communication, and surgery	The general population	N/a