Recent increases in overweight and obesity have established Metabolic–Bariatric Surgery (MBS) as a principal intervention for durable weight reduction and metabolic improvement. Given the elevated perioperative complication risk among patients with obesity, there is a growing imperative to enhance the precision of preoperative management. This review synthesizes evidence from 2020–2025 on the application of artificial intelligence (AI) to bariatric surgery preoperative assessment, focusing on machine learning (ML), deep learning (DL), and large language models (LLMs). We summarize AI applications across three domains: preoperative risk prediction and individual assessment, patient stratification and procedure selection, and preoperative education and surgical training. The evidence suggests that integrating AI into routine preoperative workflows enables individualized, quantitative estimation of high-risk complications and weight-loss prognosis, thereby optimizing risk management; it can also support patient stratification and procedure matching to facilitate patient-centered shared decision-making; and NLP- and vision-based tools promote standardization and visualization of knowledge for patients and surgeons. Overall, AI is driving preoperative assessment toward greater individualization, interpretability, and multidisciplinary coordination, but its clinical generalizability requires validation in multicenter, prospective studies.

In recent years, immune checkpoint inhibitors (ICI) have revolutionized the treatment landscape of renal cell carcinoma (RCC), significantly enhanced patient outcomes and expanded therapeutic options beyond traditional surgical and targeted approaches. In this review, we provide a comprehensive review of the current applications of ICI in RCC therapy, elucidating their mechanisms of action, clinical efficacy across both clear cell and non-clear cell subtypes, and safety profiles. We compare the effectiveness of monotherapy versus combination therapies, demonstrating how synergistic approaches can improve efficacy and durability of responses. Furthermore, we explore the integration of multi-omics technologies, such as single-cell RNA sequencing and spatial transcriptomics, which are pivotal in predicting ICI response and addressing resistance mechanisms. Our discussion also addresses the critical challenges in RCC immunotherapy, including primary and acquired resistance, economic and ethical barriers, and the management of immune-related adverse events. By synthesizing current research and clinical findings, this review aims to provide valuable insights for optimizing RCC immunotherapy, fostering the development of more effective and individualized treatment protocols, and ultimately improving patient survival and quality of life.