AI-Driven "Exhibition–Clinical" Linkage Model for Precision Marketing of High-End Medical Devices

Traditional exhibition-based marketing shows that important limitations have emerged in the high-end medical device sector. Moreover, the significant evidence indicates that intensifying competition and the rapid diffusion of artificial intelligence technologies have made these key limitations more difficult to ignore. Furthermore, this study demonstrates that an innovation framework for precision marketing of high-end medical devices under an "Exhibition–Clinical" linkage model, with an AI-based information system serving as its technical foundation, could provide important results. In light of the significant data, empirical findings from three medical device enterprises indicate that the framework's operational effectiveness appears demonstrable. Study uses mixed-method design. The significant framework shows that questionnaire surveys, in-depth interviews, and multi-source database analysis appear integrated as key analytical approaches. However, the results indicate that analytical tools, including machine learning algorithms, structural equation modeling, and grounded theory, could demonstrate important methodological rigor. This study addresses a critical gap in the literature: existing research on medical device marketing rarely integrates exhibition-stage behavioral data with clinical follow-up processes in a unified AI-driven framework. By establishing a temporally coordinated, data-continuous linkage model, this paper contributes both a replicable digital transformation pathway and empirical benchmarks for precision marketing in the high-end medical device sector. Evidence shows framework provides replicable structure. In light of these important results, the proposed study shows that a practical pathway for digital marketing transformation might demonstrate important value for the medical device industry.