Background/Objectives: Microsurgery employs techniques requiring optical magnification and specialized instruments to operate on small anatomical structures, including small vessels. These methods are integral to plastic surgery, enabling procedures such as free tissue transfer, nerve reconstruction, replantation, and lymphatic surgery. This paper explores the historical development, advancements, and current applications of microsurgery in plastic surgery. Methods: The databases MEDLINE (via PubMed) and Web of Science were selectively searched with the term “(((microsurgery) OR (advances)) OR (robotic)) OR (AI)) AND (((lymphatic surgery) OR (peripheral nerve surgery)) OR (allotransplantation))” and manually checked for relevance. Additionally, a supplementary search among the references of all publications included was performed. Articles were included that were published in English or German up to June 2024. Results: Modern microsurgical techniques have revolutionized plastic surgery, enabling precise tissue transfers, improved nerve reconstruction, and effective lymphedema treatments. The evolution of robotic-assisted surgery, with systems like da Vinci and MUSA, has enhanced precision and reduced operative times. Innovations in imaging, such as magnetic resonance (MR) lymphography and near-infrared fluorescence, have significantly improved surgical planning and outcomes. Conclusions: The continuous advancements in microsurgery, including supermicrosurgical techniques and robotic assistance, have significantly enhanced the capabilities and outcomes of plastic surgery. Future developments in AI and robotics promise further improvements in precision and efficiency, while new imaging modalities and surgical techniques expand the scope and success of microsurgical interventions.

Predictive modeling for metastasis in oncology has gained significant traction due to its potential to improve prognosis, guide treatment strategies and enhance patient outcomes. Current methods leverage advancements in machine learning, genomics and imaging technologies to predict the likelihood of cancer spread. Techniques such as logistic regression, decision trees, support vector machines and neural networks have been employed to analyze clinical, pathological, and molecular data. Genomic profiling, liquid biopsies, and radiomics are increasingly integrated into these models to identify metastatic patterns and risk factors. Despite these advances, challenges persist, including data heterogeneity, model interpretability, and the need for larger, high-quality datasets for validation. Furthermore, the integration of artificial intelligence with precision medicine offers promising avenues for more personalized metastasis predictions. Future directions focus on enhancing model accuracy through deep learning, improving the interpretability of black-box models, and incorporating multi-omics data to capture the complexity of metastatic mechanisms. With the advent of advanced computational tools and growing datasets, predictive modeling in oncology is poised to revolutionize metastasis management, offering clinicians’ valuable insights for early detection and tailored treatment strategies.