Integration of AI-enhanced Digital Twin and Image Recognition in Bone Tumor Surgical Education: A Short Review

This study aimed to preliminarily explore the current application status and potential value of digital twin technology (DTT) that integrates artificial intelligence (AI) and medical imaging recognition in orthopedic surgical simulation education for bone tumors, and to analyze its effectiveness in surgical planning, biomechanical analysis, postoperative management, and training, through a systematic review conducted in accordance with PRISMA guidelines. Through a short of relevant literature, the progress in the application of AI-enhanced digital twins in bone tumor orthopedic surgery in recent years is analyzed. A structured literature search and review method was adopted, searching databases such as PubMed, Web of Science, and Scopus for literature published between 2015 and 2024. The inclusion criteria were studies involving orthopedic medical students or surgeons, with content related to the integration of DTT with AI and image recognition technologies applied to surgical planning, biomechanical analysis, postoperative management, and training. The results indicate that DTT, by combining AI-driven image analysis and biomechanical modeling with high-precision three-dimensional modeling and scenario simulation, significantly improves the accuracy of surgical planning, the precision of biomechanical analysis, and the scientific rigor of postoperative management. In training, the integration of DTT with virtual reality technology and AI-based simulation engines provides students and novice surgeons with a high-fidelity virtual surgical environment, significantly enhancing their operational skills and confidence. Although most current studies are small-scale experiments, the potential of AI-enhanced DTT in orthopedic medicine has been validated. In the future, with further integration of artificial intelligence, image recognition, and biomechanical simulation technologies, “personalized scenario generation” can be achieved, and a standardized scenario library for bone tumor surgical education can be established. This brief review aimed to map the preliminary landscape of the technology's applications and explore its future potential. Its findings were expected to help address the scarcity of bone tumor cases and the uneven distribution of training resources, thereby providing critical technological support for the standardized education and remote skill training in bone tumor surgery.