Artificial Intelligence–Based Consumer Health Informatics Application: Scoping Review

• Published in: Journal of medical Internet research Vol. 25; no. 1; p. e47260
• Main Authors: Asan, Onur, Choi, Euiji, Wang, Xiaomei
• Format: Journal Article
• Language: English
• Published: Canada Gunther Eysenbach MD MPH, Associate Professor 30.08.2023; JMIR Publications
• Subjects: Artificial Intelligence; Biomedical Technology; Chatbots; Clinical decision making; Clinical outcomes; Computers; Consumer Health Informatics; Consumers; Decision makers; Emotions; Global health; Health informatics; Health status; Humans; Knowledge; Librarians; Literature reviews; Machine learning; Medical personnel; Medical technology; Natural language processing; Patient-centered care; Patients; Perceptions; Personal health; Precision medicine; Review; Robotics; Subject heading schemes; Telemedicine; Vision systems; Web portals; United States > US; digital health; mobile health; mobile phone; mHealth; patient outcomes; consumer informatics; machine learning; personalized health care; artificial intelligence
• DOI: 10.2196/47260
• ISSN: 1438-8871; 1439-4456

There is no doubt that the recent surge in artificial intelligence (AI) research will change the trajectory of next-generation health care, making it more approachable and accessible to patients. Therefore, it is critical to research patient perceptions and outcomes because this trend will allow patients to be the primary consumers of health technology and decision makers for their own health. This study aimed to review and analyze papers on AI-based consumer health informatics (CHI) for successful future patient-centered care. We searched for all peer-reviewed papers in PubMed published in English before July 2022. Research on an AI-based CHI tool or system that reports patient outcomes or perceptions was identified for the scoping review. We identified 20 papers that met our inclusion criteria. The eligible studies were summarized and discussed with respect to the role of the AI-based CHI system, patient outcomes, and patient perceptions. The AI-based CHI systems identified included systems in mobile health (13/20, 65%), robotics (5/20, 25%), and telemedicine (2/20, 10%). All the systems aimed to provide patients with personalized health care. Patient outcomes and perceptions across various clinical disciplines were discussed, demonstrating the potential of an AI-based CHI system to benefit patients. This scoping review showed the trend in AI-based CHI systems and their impact on patient outcomes as well as patients' perceptions of these systems. Future studies should also explore how clinicians and health care professionals perceive these consumer-based systems and integrate them into the overall workflow.