Recently, the Internet of Things (IoT) has played a powerful role in healthcare. However, the rapid growth of healthcare devices has produced many heterogeneous data and most of them are visual. It brings great difficulties to the calculation, cache, and transmission of data. The geographical dispersion and the dynamicity of nodes also challenge the development of healthcare IoT (HIoT). In this article, we propose an intelligent end–edge–cloud architecture for visual IoT-assisted healthcare systems (intelligent V-HIoT) to improve the end-to-end performance of next-generation smart healthcare. First, we systemically analyze the characteristics of human–machine–things in end side from the perspective of data processing, then define the end intelligence, solving the problem of intelligence measurement of heterogeneous devices. Second, we propose an efficiency intelligence measurement model in the edge side and cloud side, which provides a theoretical basis for the dynamic management of edge nodes. Third, we present an end–edge–cloud framework that optimizes the efficiency of data processing and node deployment. The intelligence level of HIoT is maximized as well as intelligent management of nodes is implemented. To verify the effectiveness, we perform the experiments for different approaches. The simulation results demonstrate that the intelligent V-HIoT significantly outperforms existing approaches because the proposed method can achieve maximum intelligence level of both in many heterogeneous devices and an emergency medical situation.