Identification of Key Factors and Symmetrical Hierarchical Paths Influencing the Efficiency of Medical Human–Machine Collaborative Diagnosis Based on DEMATEL-ISM

Against the backdrop of artificial intelligence (AI) empowering the medical industry, achieving symmetric coordination between patients and medical intelligent systems has emerged as a key factor in enhancing the efficacy of medical human–computer collaborative diagnosis. This study systematically identified the factors influencing the effectiveness of human–machine collaborative diagnosis in healthcare by combining literature analysis with expert interviews, based on the Socio-technical Systems Theory. It constructed a symmetric evaluation framework consisting of 19 indicators across four dimensions: user, technology, task, and environment. An integrated DEMATEL method incorporating symmetric logic was employed to quantitatively analyze the interdependent relationships among factors and identify 18 key factors. Subsequently, ISM was applied to analyze the dependency relationships between these key factors, thereby constructing a clear multi-level hierarchical structure model. Through hierarchical construction of a multi-level hierarchical structure model, four core paths driving diagnostic effectiveness were revealed. The research shows that optimizing user behavior mechanisms and technology adaptability and strengthening dynamic coordination strategies between tasks and the environment can effectively achieve the two-way symmetric mapping of the medical human–machine system from fuzzy decision-making to precise output. This has not only improved the efficacy of medical human–computer collaborative diagnosis, but also provided a theoretical basis and practical guidance for optimizing the practical application of medical human–computer collaborative diagnosis.