Volume 2, Issue 3

The emergence of metaverse medicine has introduced critical challenges in patient identity management, data privacy, and cross-institutional data sharing. Decentralized identity (DID), a blockchain-based identity framework, provides a verifiable, trustworthy, and user-controlled solution for these challenges. This paper presents a systematic analysis of DID’s technical principles, architectural designs, and applications in healthcare, reviewing both international and domestic implementations. It further examines associated risks and challenges across technical, legal, ethical, and industrial dimensions. Future directions are discussed, including the integration of DID with privacy-preserving computing, digital twins, and AI healthcare assistants, the development of globally interoperable virtual hospital identity systems, multi-party governance models, and interdisciplinary research initiatives. The findings indicate that DID effectively addresses the triad of identity trustworthiness, privacy control, and data shareability, offering foundational support for the secure, compliant, and efficient advancement of metaverse medicine.

Key Words: decentralized identity (DID); privacy protection; data sharing; cross-institution interoperability; digital twin; multi-party governance

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

Cardiopulmonary resuscitation (CPR) is a critical life-saving skill, and its widespread adoption is closely associated with the survival rate of out-of-hospital cardiac arrest. However, traditional CPR training remains several limitations. With the rapid development of artificial intelligence, virtual reality (VR), augmented reality, and metaverse-related technologies, immersive training has emerged as a promising innovation in medical education, particularly in CPR instruction. This article reviews the research progress and challenges of VR-based CPR system, and provides ideas for future technology development.

Keywords: cardiopulmonary resuscitation; virtual reality; metaverse; medical education; immersive training; artificial intelligence; skill transfer

This research plan aims to compare the clinical application effects of large language models (such as DeepSeekGPT) and specialized disease GPT (such as BAIMGPT) in the consultation and management of obstructive sleep apnea (OSA). A multicenter real-world research design is adopted, involving 1000 OSA patients or high-risk individuals. Through user cross-sectional evaluations and third-party expert reviews, the performance of the two models in aspects such as convenience, friendliness, security, accuracy of problem understanding, accuracy of answers, voice interaction, visual empowerment, and the degree of patient needs is assessed. The research focuses on the roles of the two models in OSA screening, diagnostic accuracy, and personalized prevention and treatment, and explores their potential in enhancing patient education, doctor training, and coverage of primary medical care. The research results will provide empirical evidence for the optimized application of artificial intelligence technology in OSA diagnosis and treatment, and promote the development of precision medicine and health management.

Key Words: obstructive sleep apnea; BAIMGPT; DeepSeek GPT

This study aims to compare the clinical value of a general large language model (DeepSeek GPT) and a disease-specific optimized model (BAIMGPT) in pulmonary nodule consultation and management. Through a multicenter real-world study, 1,000 patients with pulmonary nodules from 12 hospitals will be recruited for a randomized self-controlled trial evaluating the performance of the two models across eight dimensions: convenience, friendliness, sense of security, accuracy in question comprehension, response professionalism, voice interaction, visual empowerment, and demand level. The core methodologies include dual user evaluation, third-party blinded review, and endpoint assessment. The study was expected to validate BAIMGPT’s advantages in improving screening awareness, personalized management, and physician-patient trust, providing evidence-based support for AIpowered early lung cancer screening. The protocol has passed ethical review, with anonymized data processing ensuring both innovation and safety.

Key Words: plmonary nodule; BAIMGPT; DeepSeek GPT

Named Entity Recognition, as a core task in Natural Language Processing, plays a crucial role in identifying medical entities such as diseases and symptoms in Electronic Medical Records, which is of great significance for clinical decision support and the construction of medical knowledge bases. However, traditional methods rely heavily on large amounts of annotated data and complex models, resulting in high training and inference costs. This paper proposes a generative medical NER method that integrates semantic retrieval and prompt learning with large language models. First, a sentence-level vector database is constructed to semantically encode EMRs for retrievable representations. Then, based on the input sentence, semantic similarity retrieval is performed, and similar examples are dynamically injected into a prompt template to guide the model in entity extraction. Finally, entity type annotation results are generated through structured special markers, enabling direct decoding output. Experimental results demonstrate that the proposed method performs well on both a self-constructed EMR dataset and the Ruijin Hospital diabetes dataset, and exhibits strong robustness and transferability, especially in low-resource scenarios.

Key Words: named entity recognition; electronic medical records; large language models

With the rapid development of artificial intelligence, AI tools have demonstrated significant value in medical research and academic writing, enhancing efficiency in data processing, literature retrieval, manuscript drafting, visualization, and interdisciplinary collaboration. Beyond serving as auxiliary tools, AI is increasingly becoming a research partner, contributing to hypothesis generation, experimental design, and multimodal data analysis, thereby fostering a paradigm shift toward“ human–AI coresearch”. Typical applications include rapid drafting of medical manuscripts, research integrity checks, and automated generation of imaging reports and scientific figures. However, the widespread adoption of AI also raises challenges concerning authorship, data traceability, content reliability, and privacy protection. International guidelines such as the ICMJE Recommendations and public statements from Science and Nature explicitly emphasize that AI tools cannot be listed as authors, that their use must be transparently disclosed, and that ultimate responsibility lies with human researchers. Therefore, it is urgent to establish a normative framework centered on transparency, accountability, verifiability, and compliant openness, ensuring that AI delivers both efficiency and innovation while laying the foundation for a sustainable research ecosystem in the digital era.

Key Words: artificial intelligence; medical research paradigm; data-driven science; generative models; research ethics

With the acceleration of population aging and the increasing shortage of medical resources, artificial intelligence (AI) technology has shown great potential in the field of medical care. Integrating advanced technologies such as machine learning, natural language processing, and computer vision, AI has created innovative solutions in disease monitoring, rehabilitation assistance, elderly care, and mental health. The essence of AI care lies in the application of modern information technology and AI algorithms to provide intelligent support for disease prevention, monitoring, rehabilitation and daily health management. Its goal is to achieve roundthe clock monitoring and personalized management of patients by reducing labor costs and improving the efficiency of medical resource utilization. In this process, security, humanization, privacy protection, fairness and accessibility, as well as transparency and explainability, constitute the five basic principles of AI care, leading the development of technology in the right direction. In practical applications, AI monitors physiological indicators in real time through wearable devices to warn of health risks. Intelligent robots and VR technology provide customized guidance for rehabilitation patients; AI chatbots have become a new way of psychological comfort. At the same time, AI also plays an important role in optimizing care processes and improving administrative efficiency. However, the promotion of AI care will encounter multiple challenges such as technical obstacles, social acceptance, and policies and regulations, and it urgently needs the joint help of technological innovation, ethical guidance, policy adjustment, and multi-party collaboration. When promoting the application, it is necessary to establish clear data collection and analysis standards, manual review mechanisms, equipment security guarantees, and clear identification of AI identities. At the ethical and legal level, AI care needs to draw a clear line between technical and medical responsibility, ensure transparency of informed consent, and provide special protections for vulnerable groups. In addition, data quality, continuous learning, and multidisciplinary collaboration are also key factors driving the advancement of AI care technology. In the face of technical limitations, insufficient social acceptance, and lagging policies and regulations, the future development of AI care should focus on technological innovation, ethics education, policy improvement, and international cooperation.

Key Words: artificial intelligence; machine learning; natural language processing; healthy; care; ethics