The field of document image rectification and digital twins in healthcare is experiencing significant growth, driven by advancements in machine learning and data-driven modeling. Researchers are exploring innovative approaches to improve document image rectification, including self-adaptive multitask fusion and joint low-level and high-level textual representation learning. In the healthcare domain, digital twins are being developed to enhance resource planning, patient flow, and personalized decision-making in oncology. Notable papers in this area include Document Image Rectification Bases on Self-Adaptive Multitask Fusion, which proposes a novel rectification network, and Predictive Digital Twins with Quantified Uncertainty for Patient-Specific Decision Making in Oncology, which develops an end-to-end data-to-decisions methodology for personalized medicine. Additionally, papers like BedreFlyt and CEC-Zero demonstrate the potential of digital twins and large language models in improving patient care and Chinese text processing capabilities.