Table 2.

Overview of journal papers 1-11 included in the scoping review.

Author, country	Study objective	Study design	Medical field	App type	User
Almalki [39], Saudi Arabia	Conduct an online survey to investigate factors that influence consumers’ willingness to use COVID-19 health chatbots, as well as individual differences, the likelihood of future use, and challenges and barriers that affect their motivation.	Quantitative research: questionnaire	COVID-19	Health care information inquiry	Consumers
Cirkovic [12], Germany	Determine whether the algorithms of the 4 ophthalmic self-diagnosis apps selected from the literature change over time, as well as their efficiency of diagnostic and treatment recommendations at 3 emergency levels of diagnostic outcomes.	Quantitative research: follow-up study—a long-term research project examining the degree to which effects seen shortly after the imposition of an intervention persist over time	Ophthalmology	Diagnosis	Consumers
Demner-Fushman et al [34], the United States	Develop an online consumer health question-and-answer system that provides reliable and patient-oriented answers to consumer health queries.	Quantitative research: case analysis	General practice	Diagnosis, health care information inquiry	Consumers
Esmaeilzadeh [15], the United States	Investigate the perceived benefits and risks of AIa medical devices with clinical decision support functions from the consumers’ perspective and develop models based on value perception.	Quantitative research: online survey	N/Sb	N/S	Consumers
He et al [41], China	Develop a user needs library in the medical XAIc field and design and evaluate a consumer ECGd self-diagnosis system based on the needs library.	Mixed methods study: systematic review, questionnaire, interview	General practice, ECG diagnosis	Diagnosis	Consumers
Kyung and Kwon [43], Singapore	Investigate individuals’ acceptance of AI-based preventive health interventions and changes in health behaviors compliance.	Quantitative research: questionnaire, experiment	Fitness	Health self-management	Consumers
Nadarzynski et al [46], the United Kingdom	Explore the acceptability of AI-powered health chatbots in order to identify potential barriers and enablers that could have an impact on these new types of services.	Mixed methods study: interview, questionnaire	General practice	Health care information inquiry	Consumers
Ponomarchuk et al [47], Russia	Propose a machine learning method for the rapid detection of COVID-19 using cough recordings from consumer devices and develop and deploy a mobile app for COVID-19 detection using symptom checkers and voice, breathing, and cough signals.	Quantitative research: case analysis	COVID-19	Diagnosis	Consumers
Savery et al [37], the United States	Build a question-driven and natural language automated summary data set that responds to consumers’ health inquiries.	Quantitative research: experiment	General practice	Health care information inquiry	Consumers
Scott et al [17], Australia	Determine the attitudes of physicians, consumers, administrators, researchers, regulators, and industry toward the use of AI in health care.	Systematic review	N/S	N/S	Consumers, physicians
Van Bussel et al [45], the Netherlands	Through interviews with former cancer patients and physicians, expand the unified theory of acceptance and use of technology (UTAUT) model to identify the key factors driving virtual assistant acceptance among patients with cancer.	Mixed methods study: interview, questionnaire	Cancer	Diagnosis, health self-management, health care information inquiry	Consumers

^aAI: artificial intelligence.

^bN/S: not specified.

^cXAI: explainable artificial intelligence.

^dECG: electrocardiogram.