The health of the elderly needs to be managed in four dimensions: physiological, psychological, functional and social, so as to delay decline, prevent diseases and increase longevity, and improve life satisfaction in a scientific way. However, the elderly in China face challenges such as chronic diseases, weak awareness of health management, lack of psychological services, and barriers to the use of digital tools, coupled with the increasing proportion of people living alone and empty nests, insufficient social support, and uneven medical resources, which further exacerbate health risks. New quality productivity empowers elderly care has become the key to breaking the situation, and technologies such as smart wearable and smart medical care can improve medical accessibility and management efficiency. AI robots alleviate loneliness, and digital technology enables precision health management. It can integrate long-term care insurance and community smart health care resources to form a new one-stop pension model. Intelligent monitoring and telemedicine, digital therapeutics, VR and other technologies can be applied to disease management, rehabilitation training, and optimization of treatment plans. Smart elderly care and home care combine AI nutritionists, blockchain and other technologies to provide personalized services. Data-driven can achieve precise health management, take into account privacy and security, and build closed-loop services. Community smart health care provides digital social and emotional care through intelligent environmental perception and AI health huts to optimize service quality. The expected effects include an increase in the accident recognition rate, a shortened response time, a decrease in the accident rate, and an improvement in self-care ability.

Key Words: health of the elderly; generative pre-training transformer; internet of things; artificial intelligence; augmented reality; virtual reality

Lung diseases have long been at the forefront of global mortality and disability, although chest X-ray and CT are the basic entrances for screening, diagnosis and follow-up, they are exposed to limitations such as miss diagnosis, misdiagnosis and insufficient quantification under high load and complex disease spectrum. The rise of deep learning, radiomics, and multimodal large models has made Artificial Intelligence (AI) a key driving force for chest images to move from "reading tools" to "system engineering". AI has significantly improved detection, segmentation, phenotypic quantification, and risk prediction capabilities in multi-spectrum tasks such as lung nodules/lung cancer, tuberculosis, pneumonia, interstitial lung disease (ILD), chronic obstructive pulmonary disease (COPD), small airways, and pulmonary vascular diseases, and has stabilized key indicators such as doubling time, fibrosis burden, and airway remodeling, becoming an important technical basis for the implementation of Fleischner, American College of Chest Physicians (ACCP), and China guidelines. In prevention and screening, AI supports the identification of high-risk groups, large-scale chest X-ray screening, LDCT risk stratification, and early detection of subclinical abnormalities such as ILA and small airway disease, which can be combined with health management, digital twins, and metaverse platforms to build a forward-moving defense line intervention model. Physicians and patients generate structured reports, provide "guide online" decision support, and output differentiated explanations by using imaging diagnostic models and medical GPTs. AI also empowers radiotherapy planning, preoperative navigation, treatment response prediction, and lung function estimation, promoting image-function integration and individualized long-term management for treatment and follow-up. In the future, it will focus on the construction of general chest imaging large models, the deep integration of 5P medicine, the construction of federated learning and global collaborative data networks, and move from "intelligent imaging links" to the whole course of the disease system project that connects "hospital-community-family-cloud-metaverse", so that chest X-ray and CT will become the key infrastructure of the digital respiratory health ecosystem.

Key Words: AI; Lung cancer screening; radiomics and multimodal foundation models; quantitative phenotyping of ILD and COPD; digital twin and metaverse medicine; medical GPT and intelligent decision support

As an emerging technological paradigm, the metaverse is profoundly reshaping the ecology of medical education. Grounded in the context of sports medicine education, this study employs methods such as literature analysis and logical reasoning to examine the current state of metaverse development. From an ecological perspective, it investigates the digital reconstruction process of various elements in sports medicine education driven by the metaverse and explores the integrated cultivation path of virtual and reality. Furthermore, it proposes strategic recommendations for optimizing sports medicine education governance. The study suggests that under the metaverse context, the ecology of sports medicine education presents a new landscape featuring virtual-real symbiosis, science-education collaboration, and cross-border integration. Adhering to the concept of people-oriented and technology-empowered, it is essential to promote morphological reshaping, scenario innovation, and model transformation. This involves creating an immersive and interactive learning experience, building an open and shared knowledge network, and improving the collaborative innovation mechanism for talent cultivation. By promoting deep integration of theory and practice, teaching and research, and schools and hospitals, a high-quality sports medicine talent training system with international competitiveness can be established, injecting new momentum into the integrated development of sports and medicine.

Key Words: metaverse; sports medicine education; ecosystem; digital reconstruction; governance optimization

Alzheimer’s disease (AD), as a major global public health crisis, faces multiple challenges including difficulties in early diagnosis, limited therapeutic options, and heavy caregiving burdens. The deep integration of artificial intelligence (AI) and metaverse technologies offers innovative solutions for comprehensive AD management. Immersive environments combined with AI analytics enable early screening and risk stratification; digital twins and adaptive algorithms facilitate personalized digital interventions that may slow disease progression; while immersive simulation training provides efficient support for caregivers and healthcare professionals, enhancing care quality and decision-making capacity. The AI-driven metaverse will reshape AD diagnosis, treatment, and caregiving systems, opening new pathways to overcome current obstacles.

Keywords: Alzheimer's disease; metaverse; artificial intelligence; digital therapy; digital twin

Medical new quality productive forces (MNQPF) is a relatively new concept that refers to the ability to apply modern technological tools, especially emerging technologies like the internet, big data, artificial intelligence, and the metaverse, to improve the quality and efficiency of medical services. The core of this ability lies in innovation, including technological, managerial, and service innovation. This article reviews the technical foundations and current development status of MNQPF, points out the direction for its development, and aims to contribute to the construction of a modern medical system and the realization of the Chinese Dream of national rejuvenation.

Key Words: new quality productive forces in medicine; artificial intelligence; internet of thing

The support layer technology and the feasibility and potential value of metaverse in medicine are elaborated in this paper. Through the integration of virtual reality, augmented reality, artificial intelligence and other technologies, metaverse technology has made medical services more intelligent and personalized, bringing unprecedented opportunities and challenges to the medical field. We should fully leverage the innovative advantages of metaverse technology, seize opportunities and actively respond to challenges, to achieve sustainable development and application in the medical field, promote the improvement of medical services and innovation in medical education, and bring more welfare and progress to human health and medical cause.

Key Words: metaverse in medicine; virtual diagnosis; telemedicine; health data analysis; medical education

To systematically summarize the major challenges in developing medical GPT systems and, with reference to recent international reviews, evaluation frameworks, ethical and regulatory guidance, as well as Prof Chunxue Bai' s BAIMGPT White Paper, to outline practical solutions for translating large language models into clinically usable systems. Recent high-impact systematic reviews, methodological studies, real-world workflow evaluations, and governance guidance were synthesized to examine the main issues in medical GPT development, including factual reliability, knowledge updating, data governance, multimodal integration, workflow adaptation, explainability, bias, fairness, and accountability. Current evidence indicates that the bottlenecks of medical GPT go well beyond imperfect accuracy. Major challenges include hallucinations and factual inconsistency, limited ability to absorb newly updated medical knowledge, heterogeneous clinical data and unstable labels, insufficient support for multimodal decision-making, weak adaptation to real-world workflows, incomplete explainability and accountability, and concerns regarding bias, fairness, and ethics. Current LLMs remain sensitive to information order and quantity and are not ready for autonomous clinical decision-making. The mission of medical GPT development is not simply to improve language generation, but to transform large models into trustworthy medical intelligence systems with reliable knowledge, workflow compatibility, traceability, and governance readiness. At present, medical GPT should be positioned as a tool for cognitive augmentation and workflow support rather than a substitute for clinical judgment.

Key Words: BAIMGPT/medical GPT; large language model; clinical decision support; retrieval-augmented generation; data governance; human-AI collaboration; disease-specific agent; BAIMGPT

Association for Metaverse in Medicine and the International Alliance for Metaverse in Medicine were founded and I have established an expert consensus on metaverse in medicine. In the future, the development prospects of metaverse in medicine will be even

broader. With today’s continuous technological innovations and breakthroughs, metaverse in medicine will enable more accurate, efficient, and personalized medical service. The metaverse in medicine may also be expanded to more fields, such as gene editing and drug development, to provide more comprehensive and in-depth support for medical research and treatment. Metaverse in medicine is full of potential and opportunities, and it will continue to promote the development of the medical field and make great contributions to human health.

Key Words: metaverse; metaverse in medicine; virtual reality; augmented reality; mixed reality; extended reality