The development and progress of digital technology is driving traditional medicine towards metaverse in medicine. New quality productive forces is generated by revolutionary technological breakthroughs, innovative allocation of production factors, and deep industrial transformation and upgrading. It features high technology, high efficiency, and high quality. Metaverse in medicine is both an important component of new quality production forces, and will promote the new quality productive forces to a higher level by improving health level of workers. It will also promote the formation and role of new quality productive forces on a larger scale and to a greater extent. To this end, we must consciously follow the guidance of the theory of new quality productive forces, accelerate the digitization of medical knowledge, utilize artificial intelligence to generate new medical knowledge, and bravely apply metaverse in medicine to broader fields, so as to maximize the improvement of new quality productive forces level, and promote the formation and operation of new quality productive forces.

Key Words: metaverse in medicine; new quality productive forces; workers; artificial intelligence; digital technology

Faced with the high incidence and mortality rates of lung cancer domestically, as well as issues such as over-treatment and delayed diagnosis during manual image interpretation, Professor Bai Chunxue led a team to establish clear screening criteria and target populations for lung cancer. They proposed using artificial intelligence (AI) to mitigate the shortcomings of manual image reading. However, AI also faces challenges such as data centralization and lack of sharing, which need to be further addressed. The emergence of the metaverse medicine provides a potential solution to these issues. In 2022, the International Alliance of Metaverse Medicine (IAMM) was formally established, with Professor Bai serving as the inaugural chairman of the Metaverse Medicine Founding Conference. Subsequently, he and his team published two seminal works, titled Future Come: Metaverse Medicine We Need and Metaverse Medicine, elaborating on the vision of metaverse medicine empowering lung cancer screening and management. They also founded the Clinical eHealth (CEH). We should further promote the application of PNapp5A, human-machine MDT, and metaverse empowerment to enhance the level of lung cancer screening and management, realizing the vision of “preventing disease by famous doctors, and wisdom of metaverse for all”.

Key Words: lung cancer; artificial intelligence; metaverse; screening and management

The Internet of Medical Things （IoMT） has given rise to a new medical paradigm: “Three links span all spatiotemporal dimensions, integrating four communities within. Quality control, preventive medicine, healthcare, and treatment together form a novel mode to benefit all human beings.” This paradigm facilitates medical alliances and hierarchical medical systems, enabling grass-roots doctors to better implement homogeneous healthcare practices with a strong foundation and extensive coverage, ultimately serving patients more effectively. However, in the real world, the implementation and advancement of this work still rely on experts’ promotion and implem entation. The most in-demand doctors have finite capacities, making it challenging to extend the influence of renowned physicians, serve patients on a larger scale, and benefit society more broadly. Now, with the development of the concept and related technology of metaverse, a new opportunity has arisen for the IoMT. This will help address the issue of limited capacity among top doctors by leveraging a virtual-real interaction platform and the eight major characteristics of the metaverse. Furthermore, it is possible to establish meta-hospitals thereby implementing a metaversal healthcare model that embodies “new opportunities in IoMT, direct encounters doctors in metaverse in medicine, quality control through virtual-real interaction, and unmatched integration of humans and machines.”By adopting a management model focused on “patient-centered care, emphasis on metaverse in medicine, specialized disease management, and quality assurance,” meta-hospitals can provide homogeneous healthcare that extends throughout and strengthens the primary care sector. Although there have been no recognized experiences with constructing metaverse hospitals so far, exploring and practicing this possibility while keeping in mind demand backgrounds, previous foundations, metaverse doctors, and management will undoubtedly serve as catalysts for further development. Constructing meta-hospitals contributes to the “Healthy China 2030” vision, adhering to its strategic theme of “building and sharing the health of all citizens.” 

Key Words: artificial intelligence; large model; metaverse in medicine; medical artificial intelligence

With the rapid evolution of precision oncology, particularly in non-small cell lung cancer (NSCLC), therapeutic decision-making is increasingly shaped by histologic subtype, driver alterations, immune biomarkers, and minimal residual disease (MRD). Under this paradigm, conventional pathology based solely on morphologic interpretation is no longer sufficient for modern clinical needs. The integration of artificial intelligence (AI) and digital pathology has transformed whole-slide imaging (WSI) from static glass slides into computable, sharable, and traceable data objects, enabling automated tumor region detection, histologic classification, tumor cell proportion estimation, PD-L1 quantification, tumor microenvironment analysis, and even prediction of potential molecular phenotypes. In parallel, molecular testing has expanded from a limited number of actionable genes to broad multigene panels, while liquid biopsy and circulating tumor DNA (ctDNA) provide complementary options for molecular profiling when tissue is limited. MRD monitoring further shifts lung cancer management from one-time pretreatment stratification toward dynamic peri-treatment risk assessment. This review systematically summarizes the roles of AI-assisted pathology interpretation, the integration of driver mutations with PD-L1, TMB, ctDNA and MRD, the coupling of digital pathology with molecular subtyping, the importance of data standardization in precision medicine, and the major barriers to clinical translation, including insufficient external validation, platform heterogeneity, limited interpretability, regulatory concerns, and fragmented workflows. We argue that the true value of AI-enabled digital pathology and molecular testing lies not merely in improving the accuracy or efficiency of individual diagnostic steps, but in establishing an intelligent companion diagnostic system spanning the entire continuum of lung cancer care. Such a system can continuously integrate pathology, molecular profiling, liquid biopsy, MRD surveillance, and clinical decision-making. Looking forward, the field is expected to evolve from single-task algorithms to multimodal foundation models, from static companion diagnostics to dynamic companion diagnostics, and from isolated laboratory tools to regionalized, platform-based intelligent ecosystems, ultimately promoting data-driven precision lung cancer care.

Key Words: lung cancer; digital pathology; artificial intelligence; molecular testing; companion diagnostics

Vascular anastomosis, as one of the core surgical techniques, directly determines clinical efficacy in trauma repair, organ transplantation, and vascular reconstruction. This paper systematically reviews the development and current research status of vascular anastomosis techniques such as traditional manual sutures, robotic-assisted technologies, biomedical adhesives, and energy welding. Traditional manual sutures, regarded as the gold standard of vascular anastomosis, have achieved ultra-precise anastomosis at the 0.1-mm level through advancements in microsurgical techniques, making them the most clinically prevalent method. However, they are limited by issues such as foreign body retention, high demands on surgeon, prolonged operative times, and high postoperative stenosis rates. Robotic-assisted systems offer enhanced precision in complex anatomical regions, achieving submillimeter accuracy. However, their widespread adoption is constrained by high costs, reliance on suturing, and steep learning curves. Biomedical adhesives and energy welding techniques significantly reduce operative time but are not yet clinically applicable due to insufficient anastomotic strength. Although the GEM Coupler stapling devices have been clinically applied, other stapling technologies remain limited in scope, with ongoing research in structural designs and biodegradable materials. Future advances in vascular anastomosis are expected to focus on three directions: material innovation, technological breakthroughs, and clinical translation.