Abstract: With the aging of the global population, the incidence of venous thromboembolism (VTE) has increased significantly in the elderly population, and its prevention and treatment face many challenges, including the complexity of age-specific risk factors and the limitations of traditional assessment tools. This paper systematically reviews the research progress of artificial intelligence (AI) and wearable devices in the prevention and treatment of VTE in the elderly. In terms of prediction and prevention, the AI model significantly improves the accuracy of risk identification by integrating multi-source data, with an AUC value of 0.88. Wearable devices such as smart insoles, millimetre-wave radar, and flexible e-skin patch achieve the early detection of VTE through monitoring the physiological indexes such as gait variations, lower limb micromovement frequency, and temperature gradient, achieving early warning of VTE and supporting the development of personalised interventions. In terms of diagnosis and assessment, handheld bedside ultrasound (POCUS) and wearable ultrasound systems combined with AI have improved the efficiency and accuracy of diagnosis. Decision support tools based on electronic health records (EHR) and real-time physiological parameter monitoring by smart devices optimise risk assessment and health management strategies. On the therapeutic side, AI technology guides the development of individualised anticoagulation strategies and evidence-based decision-making for inferior vena cava filters, and dynamic monitoring of wearable devices combined with behavioural interventions effectively reduces the incidence of VTE. Although AI and wearable devices have great potential in the prevention and treatment of VTE in the elderly, data privacy protection, differences in the acceptance of technology in the elderly population, and uneven medical resources are still the main barriers to the promotion of the technology. In the future, it is necessary to build a smart health management platform integrating data from multiple sources, strengthen the privacy protection mechanism, optimize the aging-adapted design of the devices, and promote the translation and application of the technology through multicenter clinical validation, in order to achieve a more efficient and safe system of intelligent prevention and treatment for VTE in the elderly.

Key words: artificial intelligence, wearable devices, venous thromboembolism, aged, personalized medicine