With the rapid development of the internet and artificial intelligence technologies, medical AI has demonstrated significant potential in areas such as image diagnosis and robotic assistance, providing greater assurance for patient health. Although the widespread adoption of medical AI is inevitable, it has not yet been extensively implemented. To address this, we have constructed a model for consumer intention to use medical AI based on the Technology Acceptance Model (TAM), incorporating subjective norms, trust, and external factors. We collected 684 valid questionnaires through online and offline methods and analyzed the data using SmartPLS 3.0 software and SPSS data processing software for reliability and validity analysis. We also tested the overall fit of each indicator variable and determined whether the hypothetical model was valid through path coefficients and p-values. The empirical results allow us to infer the factors influencing the promotion of medical AI and to propose suggestions for the development and improvement of medical AI.

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A Study on Willingness to Use Healthcare AI Based on TAM Modelling

  • Huiying Du,
  • Weijie Han,
  • Chen Yao,
  • Genxiang Gao

摘要

With the rapid development of the internet and artificial intelligence technologies, medical AI has demonstrated significant potential in areas such as image diagnosis and robotic assistance, providing greater assurance for patient health. Although the widespread adoption of medical AI is inevitable, it has not yet been extensively implemented. To address this, we have constructed a model for consumer intention to use medical AI based on the Technology Acceptance Model (TAM), incorporating subjective norms, trust, and external factors. We collected 684 valid questionnaires through online and offline methods and analyzed the data using SmartPLS 3.0 software and SPSS data processing software for reliability and validity analysis. We also tested the overall fit of each indicator variable and determined whether the hypothetical model was valid through path coefficients and p-values. The empirical results allow us to infer the factors influencing the promotion of medical AI and to propose suggestions for the development and improvement of medical AI.