Volume 3, Issue 2

Lung cancer remains the leading cause of cancer-related mortality worldwide and poses an especially severe burden in China. Low-dose computed tomography (LDCT) screening has significantly improved the detection rate of early-stage lung cancer; however, it has also introduced major challenges, including increased false-positive findings, overtreatment, difficulties in long-term follow-up, and regional disparities in healthcare resources. In recent years, the rapid development of artificial intelligence (AI), large language models (LLMs), the medical Internet of Things (MIoT), and Metaverse Medicine has driven the evolution of Medical AI Agents from simple imaging-assistance tools into novel digital medical entities capable of perception, reasoning, decision-making, execution, feedback, and continuous learning. Pulmonary Nodule Agents represent a new generation of digital medical systems built upon multimodal data integration, medical GPT technologies, knowledge graphs, and continuous digital healthcare frameworks. These systems can perform risk identification, dynamic stratification, pathway recommendation, long-term follow-up, and closed-loop management for pulmonary nodules, thereby enabling precision control of lung cancer risk throughout the entire clinical pathway. Currently, AI has evolved from traditional computer-aided detection (CAD) systems toward multi-agent collaborative architectures and is increasingly being integrated into advanced clinical scenarios, including digital multidisciplinary team (MDT) management, Hospital at Home, digital twins, and Metaverse Medicine. Drawing upon international research, consensus guidelines, and the BAIMGPT and PNapp 5A framework proposed by Professor Chunxue Bai’s team, this article systematically reviews the concepts, technological foundations, core architectures, clinical applications, real-world challenges, and future development trends of pulmonary nodule agents. Particular emphasis is placed on the role of AI in pulmonary nodule detection, risk stratification, multimodal integration, continuous follow-up, grassroots healthcare empowerment, and real-world governance. Evidence suggests that pulmonary nodule agents are not merely managing “nodules on imaging,” but rather the dynamic future risk of lung cancer in individual patients. In the future, such intelligent agents are expected to transform pulmonary nodule management from a paradigm of “detecting nodules” to one of “precision risk management,” ultimately advancing the vision of “preventive medicine by renowned physicians and universal healthcare enabled by metaverse medicine.”

Key Words: artificial intelligence; intelligent agent; pulmonary nodule; early lung cancer screening; medical GPT; multimodal integration

Asthma management is shifting from the traditional model based predominantly on intermittent outpatient assessments toward a precision management model centered on continuous monitoring, dynamic early warning, stratified intervention, and outcome tracking. The Respiratory Internet of Things (Respiratory IoT), leveraging intelligent inhalers, home pulmonary function testing, pulse oximeter, wearable devices, environmental sensors, mobile terminals, and cloud platforms as its primary components, creates an integrated service network connecting hospital wards, outpatient clinics, community settings, and home environments. Research conducted by Professor Bai Chunxue’s team on metaverse medicine, new-quality productivity in medicine, and BAIMGPT (Bai’s Medical GPT) provides a theoretical and technical framework with distinct Chinese contextual characteristics，supporting the workflow of "multi-source sensing, intelligent analysis, digital human interaction, quality control, and closed-loop execution."This paper systematically reviews the application of the Respiratory IoT in inpatient asthma monitoring, chronic disease management, and hospital-community-home collaboration, summarizing research progress, practical challenges, and future directions for precision monitoring and closed-loop management in asthma, with the aim of informing the development of a continuous and integrated care system for respiratory diseases.

Key Words: asthma; respiratory internet of things; precision monitoring; closed-loop management; smart inhalers; digital medicine

Case analysis reporting is an important approach for general practitioners to transform clinical practice, diagnostic reasoning, follow-up observations, and reflective learning into sharable medical knowledge, with substantial clinical, educational, and research value. High-quality case analysis reporting can improve first-contact recognition, referral decisions, chronic disease management, and regional quality improvement, while also serving as an effective vehicle for case-based teaching, young physician training, and real-world evidence generation. However, in routine practice, general practitioners often face multiple barriers, including limited consultation time, incomplete data collection, weak diagnostic reasoning frameworks, insufficient standardized writing skills, difficulty in evidence retrieval, and low research conversion efficiency. Recent advances in generative artificial intelligence, large language models, natural language processing, multimodal AI, ambient clinical documentation tools, knowledge graphs, Internet of Things, and metaverse medicine have created new opportunities for empowering case analysis reporting in primary care. AI can support history taking, structured data extraction, reconstruction of disease timelines, problem representation, differential diagnosis prompting, evidence retrieval, case-based educational design, case repository development, and research transformation, thereby improving the completeness, standardization, interpretability, and reusability of case reports. Current studies suggest that AI has shown promising performance in complex diagnostic reasoning, clinical text generation, medical education, and documentation assistance. Nevertheless, real-world implementation in primary care remains constrained by hallucinations, bias, privacy risks, unclear accountability, limited external generalizability, and the potential erosion of clinicians’ independent reasoning ability. Looking forward, AI empowerment in primary care case analysis reporting should follow the principles of human-AI collaboration, physician leadership, factual verifiability, auditability, and gradual scenario-based deployment. The ultimate goal is not merely to help physicians write faster, but to build an intelligent case ecosystem that integrates clinical care, education, research, quality assurance, and regional knowledge sharing.

Key Words: artificial intelligence; general practitioners; case analysis reporting; large language models; medical education; real-world research

The management of pulmonary nodules is gradually shifting from traditional "single image judgment" to "continuous risk management" mode, especially in the fields of ground glass nodules and microinvasive lung adenocarcinoma. The importance of artificial intelligence (AI) and medical GPT assisted decision-making is becoming increasingly prominent. This case presents a typical case where postoperative pathology was successfully corrected through BAIGPT pulmonary nodule intelligent agent evaluation. The patient is a 67 year old female with no smoking history. She visited Professor Bai's AI clinic in July 2025 due to mixed nodules in the left lung. The AI system evaluation showed that the probability of malignancy of the nodule was as high as 88%. Although the first postoperative pathological report indicated "no evidence of malignant tumor", after AI assisted pathological review and molecular testing, it was ultimately confirmed to be microinvasive lung adenocarcinoma. This case suggests that pulmonary nodule intelligent agents not only have the ability to "detect lesions", but are also entering a new stage of "cognition reasoning error correction closed-loop follow-up", which has important clinical value in reducing missed diagnosis of early lung cancer.

Key Words: pulmonary nodule; pathology; agent

Chronic respiratory diseases are characterized by prolonged courses, fluctuating symptoms, complex care pathways, and a substantial reliance on out-of-hospital management, placing high demands on patients’ disease understanding, operational skills, self-monitoring, and long-term adherence. Patient education and management are the core determinants of symptom control, exacerbation prevention, rehabilitation participation, and improvement of long-term outcomes. Recent advances in artificial intelligence, medical large language models, digital humans, virtual experts, the Internet of Things, and metaverse medicine, are reshaping respiratory health and management from one-time in-clinic instruction toward a model that is continuous, individualized, contextualized, and interactive. In chronic respiratory disease management, these technologies of digital humans and virtual experts have shown promise in disease cognition building, inhaler instruction, interpretation of written action plans, pulmonary rehabilitation training, remote follow-up, and long-term health support, creating new opportunities for integrated hospital-community-home care. Nonetheless, current evidence remains constrained by substantial heterogeneity, a lack of hard clinical endpoints, limited adaptation for older adults and people with low health literacy, insufficient algorithmic transparency, and unclear boundaries of responsibility. The concepts of metaverse medicine, medical GPT, and BAIMGPT proposed by Professor Chunxue Bai and colleagues provide important theoretical and technical support for localized practice in this field. Therefore, this study aims to integrate recent international reviews, landmark studies, consensus guidelines and research from Professor Bai’s group, and systematically review the theoretical foundation, core applications, technical system, challenges and future directions of digital humans, virtual experts and patient education in respiratory health communication, so as to inform innovative strategies for chronic respiratory disease management.

Key Words: digital human; virtual expert; BAIMGPT; internet of things; metaverse medicine; patient education and management; chronic respiratory disease

Medical education is moving beyond a model dominated by classroom teaching and opportunistic bedside exposure toward one characterized by contextualization, interactivity, continuity, and competency-based learning, driven by the rapid development of extended reality, artificial intelligence, digital humans, virtual patients, digital twins, and the Internet of Medical Things. Metaverse medicine should not be understood as a single device or software platform; rather, it represents an integrated educational and clinical ecosystem built on immersive interaction, virtual-real fusion, real-time connectivity, and data-driven learning. Respiratory education is particularly well suited to this transformation because it involves complex knowledge structures, dynamic monitoring, procedure-intensive training, and strong reliance on teamwork and workflow coordination. Current evidence suggests that XR/VR/AR, virtual patients, simulation-based education, and large language model-assisted teaching can improve knowledge acquisition, procedural performance, learner engagement, and clinical reasoning, although stronger evidence is still needed regarding long-term transfer, real-world clinical outcomes, and cost-effectiveness. In respiratory education, digital human teachers, virtual cases, virtual wards, bronchoscopy and EBUS simulation, immersive training for respiratory failure recognition, and mechanical ventilation education appear to be the most practice-relevant and immediately actionable scenarios. In parallel, concepts proposed by Professor Chunxue Bai’s team—including metaverse medicine, medical GPT, and BAIMGPT—provide an important localized theoretical foundation and implementation pathway for the intelligent upgrading of respiratory education in China. This review summarizes the theoretical basis, major application scenarios, educational value, practical challenges, and implementation strategies of metaverse medicine in respiratory education, with the aim of informing future educational reform and talent development in respiratory medicine.

Key Words: metaverse medicine; respiratory education; digital human teacher; virtual case; virtual ward; immersive clinical training; BAIMGPT

With the advancement of metaverse technology and the successful commercialization of head-mounted displays, the application prospects of the metaverse in medical education are increasingly prominent. This article systematically reviews the latest advancements in the application of the metaverse in otolaryngology and head and neck surgery, and proposes ideas for specialty construction. VR surgical simulators, 3D printing combined with VR technology, can significantly improve trainee assessment scores and operational confidence, optimizing hand-eye coordination and spatial perception abilities. AR microscope systems enable contactless interaction through gesture recognition, enhancing surgical efficiency. AR navigation platforms demonstrate real-time 3D visualization advantages in craniofacial surgery, and VR and 360° video training effectively improve the anterior nares packing skills of junior doctors. VR-assisted teaching for peritonsillar abscess drainage significantly enhances medical students' structured clinical examination scores, operational confidence, and engagement. Integrating 3D virtual models and image overlay technology in transoral robotic surgery provides practical value for image-guided surgery. VR technology can effectively alleviate pain and anxiety in patients undergoing otolaryngological surgery, improve satisfaction, and serve as a non-pharmacological intervention to reduce opioid use. VR combined with wearable devices can reduce postoperative opioid consumption, and VR vestibular rehabilitation training can enhance treatment compliance and quality of life for patients with otolith disorders. VR has become an innovative component of ENT digital teaching, demonstrating equivalent or even superior effects compared to traditional methods in anatomy, physiology, and pathology teaching. Based on these advancements, this article proposes the idea of relying on the "Fudan Zhongshan Huisheng Zhiyu" metaverse platform to build a specialized teaching system for otolaryngology: constructing high-fidelity 3D digital twin models and a structured case resource library, forming a tiered training path covering institutional education, post-graduation education, and continuing medical education, promoting the dissemination and sharing of high-quality teaching resources, and driving the innovative development of otolaryngology medical education in China.

Key Words: full-cycle medical education; training and management; Fudan Zhongshan Huisheng Zhiyu; metaverse platform; virtual reality

Specialty education in neurology faces structural bottlenecks including insufficient training in low-frequency, high-risk clinical scenarios, unequal opportunities for performing key procedures, difficulty in replicating ethically sensitive communication situations, and a weak cross-stage evaluation system. Based on the “Huisheng Intelligent Education” metaverse platform of Zhongshan Hospital, Fudan University, this paper proposes a conceptual framework for building a neurology-specific metaverse platform targeting full-cycle medical education, training, and management. Using extended reality (XR) and artificial intelligence (AI) as the technological foundation, and focusing on core teaching scenarios such as neurological examination, emergency decision-making in stroke, status epilepticus management, long-term care of Parkinson‘s disease, and breaking bad news, the platform constructs four major modules: a neurology knowledge ecosystem, immersive virtual teaching scenarios, a specialized teaching intelligence system, and a full-cycle teaching management hub. The platform supports cross-campus synchronous teaching and process tracking, aiming to establish a new paradigm of specialty education characterized by “scenario-based training — formative evaluation — personalized guidance — governance closed loop”. Taking the “acute ischemic stroke thrombolysis decision-making metaverse teaching unit” as an example, this paper demonstrates the feasible approach of the platform in scenario design, task chain organization, and technical implementation. This paper provides a replicable engineering framework and design reference for the systematic construction of metaverse teaching in neurology, with future effectiveness validation to be conducted based on actual operational data.

Key Words: neurology; metaverse in medicine; full-cycle medical education; immersive training; thrombolysis decision-making; competency evaluation

The construction of collaborative “Flagship” hospitals integrating Chinese and Western medicine has placed higher demands on talent cultivation in integrated medicine, inheritance of the experience of renowned veteran TCM experts, innovation in collaborative diagnostic and therapeutic models, and information-based support. As one of the first batch of national pilot institutions for collaborative “Flagship” hospitals integrating Chinese and Western medicine, Zhongshan Hospital, Fudan University, has already established a full-cycle framework spanning undergraduate medical education, postgraduate medical education, and continuing medical education based on the “Huisheng Intelligent Education” metaverse platform. On this basis, an innovative teaching module for Traditional Chinese Medicine (TCM) inheritance is proposed to address the current gap whereby the existing platform primarily serves modern medical education and provides insufficient support for Western medicine physicians receiving TCM training and senior TCM physicians in the cultivation of TCM inheritance and innovation as well as collaborative Chinese-Western medicine competencies. Relying on the four existing platform foundations—namely the metaverse medical knowledge ecosystem, metaverse virtual teaching scenarios, comprehensive teaching intelligent agents, and the full-cycle teaching management hub—the module is designed around four core components: a metaverse teaching module for renowned veteran TCM experts’ studios, a virtual outpatient module for collaborative Chinese-Western medicine practice, a collaborative ward-round/MDT module integrating Chinese and Western medicine, and a TCM appropriate techniques and situational training module. Together, these form a continuous teaching chain of “inheritance resource accumulation—outpatient collaborative training—complex case collaborative decision-making—application of appropriate techniques.” Furthermore, taking the metaverse teaching module for the studio of Shanghai Famous TCM Expert CAI Dingfang as an example, this paper illustrates the specific design for knowledge resource integration, reconstruction of teaching scenarios, intelligent-agent support, and process-based management and evaluation. This module is expected to provide a new practical pathway for the digital transformation of TCM inheritance and innovative collaborative training models in the context of collaborative “Flagship” hospitals integrating Chinese and Western medicine.

Key Words: huisheng intelligent education; metaverse; collaborative “Flagship” hospital integrating Chinese and western medicine; TCM inheritance and innovation; teaching module

In the context of the ongoing advancement of precision medical education, governance in medical education within teaching hospitals has gradually shifted from traditional experience-based management to data-supported management. However, current approaches still largely remain at the stages of educational evidence-chain construction, profiling analysis, and risk prediction, with relatively limited capacity to support intervention consequence simulation, governance strategy comparison, and system-level optimization. Against this background, the concept of the medical education governance digital twin (MEGDT) is proposed. By integrating digital twin theory, data governance practices in teaching hospitals, and international frontier cases, this paper discusses the conceptual connotation, implementation framework, and governance value of MEGDT. The potential value of MEGDT lies not only in enhancing the dynamic perception, simulation, and feedback capabilities of medical education governance, but also in providing decision support for teaching hospitals to achieve a better balance among educational effectiveness, resource input, organizational efficiency, and educational equity. At present, MEGDT remains at the stage of conceptual proposal and pathway exploration. Future work should prioritize minimum viable prototype studies in scenarios such as postgraduate medical education, while also addressing data quality, model credibility, privacy and security, and cost-effectiveness balance.

Key Words: precision medical education; digital twin; medical education governance; evidence-informed decision-making

Standardized rehabilitation training for patients with coronary artery disease following coronary revascularization surgery is a core therapeutic strategy to improve postoperative cardiac function, boost exercise tolerance and optimize long-term prognosis. However, mainstream cardiac rehabilitation methods currently suffer from three prominent limitations: monotonous training scenarios, poorly individualized regimen titration, and poor adherence to home-based exercise regimens. In recent years, alongside iterative advances and technical maturation in artificial intelligence (AI), the Zhongshan-specific metaverse platform featuring virtual-physical integration has offered novel insights into overcoming bottlenecks in cardiac rehabilitation training. Leveraging metaverse-based immersive interactive technology and wearable dynamic electrocardiogram (ECG) monitoring technology, this study aims to develop an integrated intervention system tailored to standardized cardiac rehabilitation for coronary artery disease patients post coronary revascularization. It further defines the system’s implementation workflow, risk control protocols and clinical eligibility criteria. The research adopts four methodological approaches: system architecture development, scenario-based modular design, alignment with evidence-based clinical guidelines, and hierarchical competency-based teaching modeling. The postoperative cardiac rehabilitation framework consists of three core functional modules: virtual reality (VR) immersive exercise training, wearable real-time dynamic ECG data acquisition, and AI-powered ECG risk early warning. Rehabilitation phases are stratified according to patients’ postoperative functional capacity. On this basis, we established individualized immersive rehabilitation scenarios, graded exercise intervention thresholds, a tiered judgment system for ECG abnormalities, and standardized emergency response workflows. Meanwhile, to meet clinical teaching demands in cardiac rehabilitation departments, this study explores a sustainable specialty teaching transformation model supported by the Fudan Zhongshan Huisheng Intelligent Education platform. The findings will provide a comprehensive theoretical framework, technical roadmap and empirical evidence for the large-scale clinical translation of this innovative cardiac rehabilitation protocol.

Key Words: metaverse; immersive exercise training; cardiac rehabilitation; medical teaching transformation