prospective assessment model of end users for artificial intelligence applications: a systematic review

Background: end-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.objective: this 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.methods: a systematic review was conducted in pubmed in 2023 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.results: out of 3714 records, 19 papers were included in the study that were published between 2019 and 2022. 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. conclusions: the 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.