Table 4.

Overview of conference papers (n=9) included in the scoping review.

Author, country	Study objective	Study design	Medical field	App type	User
Ameko et al [13], the United States	Develop a treatment recommendation system for emotion regulation using data from participants with high social anxiety to evaluate the effectiveness of emotion regulation strategies.	Quantitative research: experiment	Emotion regulation	Health self-management	Consumers
Baldauf et al [51], Switzerland and Austria	Conduct an online survey to investigate consumers’ overall willingness to use, trust factors, and desired characteristics for 4 types of AIa-powered self-diagnosis apps with different data collection and processing methods.	Quantitative research: questionnaire	Skin disease, pneumonia, heart disease, sleep problems	Diagnosis	Consumers
Gupta et al [40], India	Develop a prediagnosis system that predicts potential diseases based on a patient’s symptoms and physical measurements.	Quantitative research: case analysis	General practice	Diagnosis	Consumers
Iqbal et al [42], India	Propose a new AI-based model for active surveillance of COVID-19.	Quantitative research: case analysis	COVID-19	Diagnosis	Consumers, health departments
Oniani et al [35], the United States	Use a language model to automatically answer COVID-19–related queries and conduct qualitative evaluations.	Qualitative research: expert assessment	COVID-19	Health care information inquiry	Consumers
Park et al [44], Korea	Develop a real-time monitoring system for stroke attacks based on Internet of Things sensors and machine learning technology.	Quantitative research: case analysis	Stroke	Health self-management	Consumers, health departments
Su et al [36], the United States	Examine how AI is explained in the descriptions of 40 prevalent mobile health (mHealth) apps that claim to use AI, as well as how consumers perceive these apps.	Systematic review	Fitness, mental health, meditation and sleep, nutrition and diet, pregnancy or menstruation tracking	Diagnosis, health self-management, health care information inquiry	Consumers
Sellak et al [50], Australia	Design a model aimed at understanding how to design digital health interventions that can change lives, as well as which software design components enhance consumers’ acceptance, adherence, and sustained engagement.	Quantitative research: case analysis	Fitness	Health self-management	Consumers
Tsai et al [38], the United States	Examine how explanations can be used to improve the diagnostic transparency of online symptom checkers.	Mixed methods study: interview, experiment, questionnaire	COVID-19	Diagnosis	Consumers

^aAI: artificial intelligence.