Adoption intention and influencing factors of users of intelligent diagnosis and treatment system: a study based on questionnaire data

Background The application of artificial intelligence technology in the medical industry has achieved significant phased results. As an important technological achievement of smart healthcare, intelligent diagnosis and treatment systems have become a key carrier for innovating and improving the quality and efficiency of medical and health services. The intention of users to adopt them is a key factor affecting the widespread use of intelligent diagnosis and treatment systems. Objective This study aims to investigate the factors influencing users’ adoption of intelligent diagnosis systems among patients and healthcare professionals. Methods Based on extended TAM, UTAUT, and TTF models, we construct targeted questionnaires, and the hypothesized pathways constructed in the study are analyzed and tested using software SPSS 27 and Smartpls 4, employing structural equation modeling (SEM) with bootstrapping (5,000 samples) to test hypotheses. Results A total of 430 valid questionnaires are collected. It is found that adoption intention is influenced by different factors, and the adoption drivers differ significantly between groups. For patients, performance expectancy (β = .273, p  < .001), health literacy (β = .235, p  = .001), and technology trust (β = .129, p  = .01) significantly predict the adoption. For medical professionals, digital literacy (β = .119, p  = .03), performance expectancy (β = .145, p  = .001), and facility condition (β = .179, p  = .001) are key drivers. Privacy concern negatively impacts patients (β = .205, p  < .001), while human-machine collaboration shows no significant effect on professionals ( p  > .05). Conclusions Based on the above findings, the study provides tailored strategies guidance for developers of intelligent diagnosis and treatment systems, medical institutions, and policy makers, such as enhancing diagnostic accuracy and privacy for patients, and improving workflow integration for professionals, and points out future research directions.