Scholarlink

<p><i>Background</i> Artificial intelligence (AI), especially computer vision (CV), is rapidly changing the way medical diagnoses are performed by improving accuracy and efficiency. However, the integration of AI into the healthcare system still faces many barriers related to knowledge management, user trust, and practical applicability. <i>Objective</i> This study aims to evaluate the factors influencing behavioral intention to use machine vision (MV) in medical diagnosis and its impact on sustainability in healthcare. <i>Methods</i> The research methodology combines three techniques: Multi-criteria decision-making (MCDM), structural equation modeling (PLS-SEM), and artificial neural networks (ANN). Survey data were collected from 520 physicians, patients, and AI experts. Key. <i>Results</i> The results indicate that knowledge management factors (such as knowledge innovation, development, application, and maintenance) in conjunction with user perceptions (usefulness, ease of use, personalization, and emotional intelligence) have a significant influence on the intention to use machine vision. At the same time, this intention also plays a strong mediating role in promoting healthcare sustainability. The combined model also improves predictive accuracy and identifies key factors in the AI implementation process. Implications: The research results provide a practical basis for policymakers and hospital managers in developing effective AI technology integration strategies, contributing to promoting the development of a more sustainable healthcare system in the future.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Computer Vision in Healthcare: A Hybrid PLS-SEM and Neural Network MCDM Framework

  • 期刊论文

  • 发表日期：

    2026年7月23日

查看全文
  • Minh Ly Duc

摘要

Background Artificial intelligence (AI), especially computer vision (CV), is rapidly changing the way medical diagnoses are performed by improving accuracy and efficiency. However, the integration of AI into the healthcare system still faces many barriers related to knowledge management, user trust, and practical applicability. Objective This study aims to evaluate the factors influencing behavioral intention to use machine vision (MV) in medical diagnosis and its impact on sustainability in healthcare. Methods The research methodology combines three techniques: Multi-criteria decision-making (MCDM), structural equation modeling (PLS-SEM), and artificial neural networks (ANN). Survey data were collected from 520 physicians, patients, and AI experts. Key. Results The results indicate that knowledge management factors (such as knowledge innovation, development, application, and maintenance) in conjunction with user perceptions (usefulness, ease of use, personalization, and emotional intelligence) have a significant influence on the intention to use machine vision. At the same time, this intention also plays a strong mediating role in promoting healthcare sustainability. The combined model also improves predictive accuracy and identifies key factors in the AI implementation process. Implications: The research results provide a practical basis for policymakers and hospital managers in developing effective AI technology integration strategies, contributing to promoting the development of a more sustainable healthcare system in the future.