End Users' Prospective Assessment Model for Artificial Intelligence (AI) Applications: A Systematic Review

BackgroundEnd user opinions are crucial for the success of health applications, particularly in the emerging field of artificial intelligence (AI) in medicine. Understanding end users' perspectives is essential for the acceptance and effectiveness of AI.AimThis systematic review aims to comprehensively analyze existing literature on end users' perspectives and acceptance models for AI applications. By synthesizing and critically evaluating research, this review seeks to identify key themes, methodologies, and knowledge gaps.MethodsA systematic review was conducted in PubMed in ۲۰۲۳ to identify relevant peer-reviewed articles written in English. Inclusion criteria focused on original studies that validated assessment AI models from users' perspectives. Information extracted included publication details, countries of research, participant characteristics, data gathering and analysis methods, and attributes of the proposed models.ResultsOut of ۳۷۱۴ records, ۱۹ papers were included in the study that were published between ۲۰۱۹ and ۲۰۲۲. Participants belonged to six categories: physicians, medical students, nurses, patients, and general public. The most important assessed factors in identified papers were “ethical issues, trust, and anxiety”, “usability”, “self-efficacy and knowledge”, “social”, “benefits”, “quality of the AI products and service support”, “AI acceptance, resistance of AI, attitude, and satisfaction” were explored. In addition, the commonly examined several moderating variables, including perceived ease of use, perceived usefulness, and perceived risks. ConclusionsThe findings contribute to understanding current trends and practices in end users' perspective research. Future studies should continue exploring end users' perspectives to enhance the development and implementation of effective AI systems in healthcare.

کلیدواژه ها:

Artificial Intelligence ، Health Informatics ، evaluation frameworks ، evaluation models ، evaluation theory