Volume 2, Issue 4

Safeguarding the physical and mental health of senior secondary students is the cornerstone of all-round development, which has a profound impact on learning effectiveness, mindset shaping and future life trajectory. However, there are many challenges, such as high academic pressure, lack of sleep, lack of exercise, and weak psychological care. To solve this dilemma, GPT technology came into being, with its powerful natural language processing capabilities to help. GPT can collect physical and mental health data such as students' sleep, diet, exercise, and academic stress to achieve accurate fine-tuning of personalized guidance, making suggestions more intimate and practical. At the same time, a convenient dialogue interface is built so that students can interact with GPT at any time and enjoy instant feedback and long-term companionship. In the face of potential risks, GPT can quickly warn and propose intervention suggestions to protect students' mental health. At the psycho-educational level, GPT acts as an intelligent dialogue partner, providing students with emotional comfort, professional advice and resource links. It can also generate a variety of educational materials to meet the differentiated needs of students and improve self-adjustment through interactive applications. In psychological counseling, GPT is a powerful assistant, from the initial screening of psychological problems to the guidance of help, to the assistance of counseling, and the monitoring of social media to identify crises, to protect the mental health of students in an all-round way. Home-school cooperation is also indispensable. GPT serves as a bridge to generate mental health education content for parents, promote home-school co-education, and jointly protect students' physical and mental health. Of course, GPT applications also face challenges such as data privacy and information accuracy, so it is necessary to strengthen data protection, integrate expert wisdom to ensure that the information is correct, and continuously optimize guidance strategies. To give full play to the effectiveness of GPT, educators, technology developers, parents and all sectors of society need to work together to explore and ensure that technological progress and humanistic care are equally important, to protect the physical and mental health of students.

Key Words: generative pre-trained transformer; artificial intelligence; high school students; physical and mental health

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

Obesity/overweight has become a complex metabolic disease involving multiple systems, and the traditional model of relying on outpatient follow-up and short-term weight loss is difficult to solve the problems of insufficient prediction, extensive intervention, poor adherence and unequal resources. In recent years, artificial intelligence (AI/ML), large language models (LLM/GPT), Internet of Things (IoT), metaverse and digital humans have been introduced into the whole chain of weight management: in the prevention stage, multimodal risk models and wearable devices achieve early identification and behavior warning of high-risk groups, and metaverse scenarios improve health education and participation; In the diagnostic stage, AI supports obesity phenotype reconstruction, automatic body composition analysis, and complication “red light signal” recognition, promoting weight control from “BMI-centric” to “phenotype and complication-centric”; In the treatment and rehabilitation stage, AI-assisted accurate benefit prediction of weight loss drugs such as GLP-1, personalized push of digital therapy and behavioral coaching, full-process risk assessment of metabolic surgery, and promotion of functional recovery and anti-obesity through VR/GDTx and digital twins. At the management level, a comprehensive platform based on cloud-edge-end architecture and embedded with 5P medical concepts has been formed, and medical GPT/BAIMGPT connects doctors, patients and managers as a “weight loss digital human expert”. The main challenges include data quality and generalization, fairness and privacy protection, clinical process and payment model adaptation, and GPT security regulation. Looking forward to the future, the deep integration of AI, digital twins, and the metaverse is expected to achieve systematic improvement from simple “weight loss” to “metabolic health and self-management ability”.

Key Words: weight control; AI; Internet of Things; medical GPT; metaverse

Against the backdrop of The Lancet’s international initiative to “bring death back to life,” and in response to the pressing issue of insufficient death education in China, this paper first reviews five representative cases over the past three years in which metaverse-based medical education technologies have empowered death education — namely, brain death diagnosis, breaking bad news, advance care planning and advance directives communication, end-of-life and postmortem care, and terminal communication. Guided by relevant European, American, and Chinese guidelines, the study further proposes four core modules of death education: cognition and values, emotions and psychology, communication and relationships, and culture and behavior. These are structured into a tiered and progressive three-phase framework spanning undergraduate, postgraduate, and continuing medical education. Finally, the paper elaborates on the empowerment pathways of metaverse medical education technologies — including VR, AR, MR, and AI-enhanced virtual patients — to promote the localization, systematization, and sustainable high-quality development of death education in China through collective academic effort.

Key Words: death education; metaverse medical education technology; tiered progression; empowerment pathways; localization

Driven by the converging forces of metaverse medicine and the new quality productive forces in healthcare, respiratory medicine education must evolve from the traditional model of ‘knowledge delivery and guideline recitation’ toward a forward-looking ‘AI+ Respiratory New-Quality Productive Center’- a pioneering hub that integrates pedagogical prototyping with the incubation of interdisciplinary talent. Contemporary respiratory physicians, beyond mastering core clinical competencies, must also cultivate digital health literacy: the ability to critically understand, evaluate, and appropriately apply emerging technologies such as artificial intelligence (AI), the Internet of Things (IoT), digital twins, and extended reality (XR). Within human - AI collaborative multidisciplinary teams (MDTs) and frameworks augmented by ‘digital expert avatars,’ they should be empowered to make safe clinical decisions, conduct risk stratification, and simulate therapeutic strategies. International evidence-based medical education research indicates that AI has already been piloted in areas such as radiological interpretation, clinical skills assessment, adaptive learning, and real-time feedback. However, these efforts largely remain fragmented ‘point innovations’, lacking an integrated curriculum that embeds cross-disciplinary integration, structured design, and proactive governance. Respiratory medicine - by virtue of its inherently multimodal nature and richly visualizable longitudinal data (including imaging, pulmonary function tests, blood gas analysis, dynamic symptom trajectories, wearable-derived metrics, ventilator parameters, and sleep monitoring data) - is uniquely positioned to leverage digital twin and immersive XR scenarios to systematically address the core limitations of conventional classrooms: phenomena that are ‘invisible, difficult to simulate, and hard to reason through.’ This proposal is grounded in international consensus on digital health and AI competencies, systematically aligning with authoritative frameworks such as the Digital Education Competency Outcomes for Doctors in Europe (DECODE) and the Best Evidence Medical Education (BEME) Collaboration. It establishes a four-dimensional instructional framework encompassing Knowledge - Tools - Clinical Reasoning - Ethical Governance. At its core is an ‘AI + Case Chain’ modular curriculum: Pre-class: diagnostic pre-assessments and learner profiling to identify cognitive baselines; In-class: multimodal data visualization integrated with conversational standardized patients or interactive intelligent cases to enable immersive, context-rich clinical reasoning training; Post-class: learning analytics-driven personalized feedback loops that feed back into scholarly inquiry, creating a seamless teaching - learning- research cycle. Assessment and safeguard mechanisms center on a multi-modal evaluation system based on Objective Structured Clinical Examinations (OSCEs), adhering strictly to the principle of ‘AI-supported, never AI-substituted’ assessment. Ethical compliance and trustworthy AI governance -including data privacy protection, algorithmic bias mitigation, clear delineation of human -AI responsibility boundaries, and academic integrity - are embedded as non-negotiable safeguards. Ultimately, by leveraging interdisciplinary innovation centers and reusable digital infrastructures, this initiative transforms the traditional classroom into a next-generation smart learning environment characterized by visualizability, deep interactivity, and reasoning-driven pedagogy, systematically cultivating a vanguard cohort of talent ready to lead ‘smart respiratory care centers’ and ‘metaverse medicine’ practices by 2035.

Key Words: metaverse medicine; digital health competency; digital expert avatars; digital twin lung; multimodal learning and visualized reasoning; trustworthy AI governance and ethics

The rapid evolution of healthcare services toward virtual–physical integration, continuous interaction, and platform-based organization is fundamentally challenging the premises of traditional healthcare payment systems, which are grounded in episodic, in-person care. Unlike the mere digitization of payment channels in early digital health, metaverse medicine reconstitutes service delivery, value creation, and accountability networks, thereby exposing deep-seated structural inadequacies of existing payment systems in process measurement, real-time coordination, and multi-actor value distribution. The conventional theoretical presumption of payments as a “neutral settlement infrastructure” has become insufficient to explain and support the development of metaverse medicine. Moving beyond the “tool-neutrality” perspective, this paper repositions the payment system as an endogenous institutional arrangement within healthcare governance. Employing institutional analysis and comparative institutional analysis, it systematically examines the applicability limits of current payment instruments (e.g., CBDCs, stablecoins) in virtual–physical, process-oriented service contexts. It constructs a three-dimensional analytical framework centered on “institutional compliance – technical flexibility – depth of governance embeddedness.” The study reveals that metaverse medicine drives three fundamental shifts in the payment system: in function, from an ex-post settlement tool to a process coordination mechanism; in design logic, from uniform rules to dynamic, programmable protocols; and institutionally, from a supporting ancillary to a core governance infrastructure. Consequently, this paper argues that restructuring the payment system for metaverse medicine constitutes a cross-layer, systemic endeavor. It requires, at the institutional level, affirming its governance role; at the mechanism level, developing dynamic models for payment based on process and outcomes; and at the technical level, prudently leveraging programmable and protocol-based capabilities as enablers. The conclusion underscores that resolving payment issues is a prerequisite for the sustainable development of metaverse medicine. Its evolution is not a matter of simple technological substitution but a complex process of co-construction among technological possibilities, medical value rationality, and institutional constraints. This analysis provides a novel theoretical lens for understanding healthcare’s organizational and institutional transformations in the digital age.

Key Words: metaverse medicine; healthcare payment systems; programmable payments; payment protocols; institutional restructuring

The advent of medical robots has transformed the landscape of surgery. Compared to traditional surgical methods, the use of surgical robots provides significant advantages in enhancing precision and reducing the workload for surgeons. However, due to resource constraints, fully automated, end-to-end surgical robots are poised to become a key trend in the development of metaverse medicine. Existing knee surgery robots predominantly operate in a semi-active mode, serving as assistants to surgeons, while a limited number of "fully automated" robots can only achieve full automation during the intraoperative phase. This paper proposes an intelligent knee replacement surgery approach based on digital twin and mixed reality technologies, adopting a model of human pre-operative rehearsal and supervision, machine planning, and robotic execution. This shifts the paradigm from "semi-active" to "semi-automated" (covering the full process), serving as a transitional step toward fully automated end-to-end surgery. By ensuring safety and gaining acceptance from both medical professionals and patients, this approach enables high-quality surgical outcomes. It aligns better with current technological foundations and practical requirements while also offering potential for advancements in quantitative surgical assessment within the realm of metaverse medicine.

Key Words: knee arthroplasty; digital twin; metaverse medicine; surgical robot

The application of generative pre-trained models in the medical field is driving the transformation of medical artificial intelligence (AI) towards a knowledge-driven paradigm. While their technical potential in auxiliary diagnosis, medical Q&A, and other scenarios has been widely verified, the high sensitivity of data and decisions in medical settings makes safety and compliance indispensable prerequisites for system deployment. This study aims to systematically identify the core challenges of medical generative pre-trained models in three dimensions: data privacy, regulatory compliance, and ethical governance, and construct a comprehensive governance framework with both theoretical support and practical feasibility. First, the research analyzes the risk transmission path of model privacy re-identification, model leakage, and harmful use from the perspective of technical mechanisms. Then, combined with the characteristics of medical data, it compares and analyzes the differences in compliance requirements under the HIPAA and GDPR frameworks, as well as the core pain points and solutions of technical adaptation. Subsequently, it sorts out the institutionalization trend of global medical AI ethical principles from soft initiatives to hard supervision, and proposes an ethical evaluation matrix covering four types of risks: cognitive, operational, social, and structural. Finally, it integrates institutional boundaries, technical boundaries, and ethical bottom lines to form a multi-level governance framework covering the entire life cycle of the model. The findings demonstrate that the sustainable development of medical generative pre-trained models critically depends on the construction of a “data–responsibility” trust chain, which urgently requires the coordinated evolution of technical solutions, institutional design, and ethical awareness. The core of future industry competition is not only the competition of algorithm performance, but also the systematic competition of governance capabilities and trust mechanisms.

Key Words: Medical GPT; data privacy; HIPAA; GDPR; AI ethics

The registration of intellectual property rights in medical data is a crucial institutional arrangement for promoting the market-based allocation of data elements and advancing the development of metaverse medicine. Based on policy and legal foundations such as the “Twenty Data Articles” and considering the rapid growth trend of the global big data analytics market in healthcare, this paper conducts an in-depth analysis of the special legal requirements for medical data registration in terms of privacy protection and ethical review. By integrating three major typical cases from 2024 into the legal analysis, it systematically proposes legal pathways such as establishing a "medical data use right" and adopting a registration opposition system, providing theoretical support and practical references for constructing a medical data intellectual property registration system that meets practical needs.

Key Words: medical data; data elements; data property rights; trusted data space; medical data market