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With the advancement of computer technology and artificial intelligence, the significance of clinical decision support systems in the medical field has escalated. These systems, leveraging deep mining of multimodal data and feature fusion, furnish clinicians with precise diagnostic insights, significantly augmenting diagnostic efficiency and accuracy. This study, centered on multimodal inference, has crafted an intelligent diagnostic decision model, validated its efficacy through experimentation. It delves into the critical facets of technology adoption and application, drawing from rational behavior theory, planned behavior theory, and the technology acceptance model. Additionally, a meticulous analysis of multimodal fusion methods geared towards features, decisions, and processes has been conducted, laying a robust foundation for future investigations. The experimental segment has subjected algorithmic performance to comprehensive testing and assessment, revealing the pronounced advantage of multimodal fusion methods in enhancing diagnostic decision accuracy. This research proffers novel avenues and methodologies to propel the advancement of intelligent healthcare.
Liu et al. (Tue,) studied this question.