Background Artificial intelligence (AI) is transforming telemedicine within the broader digital-health ecosystem, yet systematic evidence on its technological hotspots and long-run trajectories remains limited. Objective To map the technological evolution of AI in telemedicine and identify innovation clusters and pathways that can inform policy, standards, and implementation planning. Methods We analysed 1451 AI-telemedicine patents (1992–2024) from the PatSnap database using a text-mining pipeline that combines classification-based social network analysis (SNA) with latent Dirichlet allocation (LDA) topic modelling. Topics were organised by life-cycle phases to trace semantic evolution. Model robustness was assessed using topic coherence and perplexity scores. Network centrality metrics (degree, betweenness, closeness) were used to identify structurally influential technologies. Results Four dominant trends emerged: (1) surgical robotics evolving from hardware optimisation to intelligent control (e.g. ‘surgical’ 0.045; ‘robot’ 0.042; ‘control’ 0.016); (2) multimodal data fusion supplanting transmission-only designs (‘patient’ 0.188; ‘site’ 0.117; ‘data’ 0.056); (3) convergence of AI and advanced connectivity (e.g. 5G) enabling personalised, patient-centred telemedicine; and (4) deep-learning image analysis extending from diagnostic support to early disease prediction (‘image’ 0.034; ‘diagnosis’ 0.022; ‘early’ 0.010). Centrality results position surgical robotics and data-fusion infrastructures as persistent long-run technological hubs. Conclusions By integrating semantic topic evolution with structural network dynamics, this study provides an empirical overview of the technological evolution of AI-telemedicine. The findings highlight several priority domains, including data-fusion infrastructure, explainable imaging AI, and surgical and remote-care applications, offering insights relevant to digital-health policy and governance.
Xiao et al. (2026) studied this question.