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

Medical quality control is very important to improve medical services, protect patients’ rights and interests, and improve medical efficiency. It is like a precise ruler to measure whether each medical procedure meets the standards. Through quality control, problems can be found and corrected in a timely manner, such as diagnostic accuracy, treatment appropriateness, medication rationality, etc., to ensure the safety, effectiveness, appropriateness and consistency of medical services. However, at present, medical quality control faces many difficulties: (1) data collection and processing are challenging, medical data are diverse, complex and scattered, and integration is difficult; (2) data analysis is difficult to use, and professional medical personnel need to interpret it in order to find problems and propose improvement plans; (3) quality control supervision is not in place, and the medical quality management system needs to be improved, and the investment is large; (4) It is difficult to make continuous improvement, which requires medical institutions to have the motivation and ability to continuously improve, and apply new quality control technologies that keep pace with the times. For example, the application of metaverse decentralized data sharing technology to medical quality control is a major innovation in the medical field, which can comprehensively improve the quality of medical services by establishing a sound quality control system, simplifying processes, strengthening supervision, and vigorously improving the quality of quality control personnel, strengthening information management and effect evaluation, and combining the real-time monitoring and feedback advantages of metaverse technology: (1) build a decentralized medical quality control platform to realize real-time collection, transmission and analysis of medical data, and ensure real-time monitoring and feedback of medical quality; (2) using decentralized technology to optimize medical processes, improve efficiency and reduce error rates; (3) implement telemedicine quality control, so that experts can remotely guide primary medical work; (4) in-depth mining and analysis of medical quality data, discovering patterns and providing basis for continuous improvement; (5) carry out medical quality education and training to improve the quality awareness and skills of medical staff; (6) carry out medical quality prediction and early warning, analyze data in real time, and grasp the quality trend. Although the Internet of Things and metaverse decentralization technology have brought new opportunities for medical quality control, there are still some challenges, such as immature technology, low user acceptance, data security and privacy protection, and cost.

Key Words: medical quality control; Internet of Things; metaverse; artificial intelligence; machine learning

Cell death is a fundamental process influencing tissue homeostasis and recovery following surgical trauma. In the perioperative context, regulated cell death aids in clearing damaged cells and initiating repair. However, excessive or dysregulated cell death—often exacerbated by ischemia-reperfusion injury, surgical stress, and anesthetic agents—can trigger intense inflammatory responses, leading to complications such as postoperative organ dysfunction (e.g., acute lung injury, delirium, and myocardial injury), impaired wound healing, and increased risk of infections. Mitochondrial dysfunction, a key contributor to these processes, releases damage-associated molecular patterns, further amplifying perioperative inflammation. Emerging evidence suggests that modulating specific cell death pathways (e.g., apoptosis, pyroptosis, necroptosis) and associated inflammatory signaling (e.g., via NLRP3 inflammasome or TLR4 inhibition) may offer novel strategies to improve perioperative outcomes. This review explores the interplay between cell death and inflammation, with emphasis on perioperative relevance, and discusses emerging therapeutic targets that may facilitate precision medicine in surgical patients.

Perioperative medicine is a series of medical activities throughout the perioperative period, including preoperative optimization, intraoperative safety, postoperative rehabilitation, and other activities. Anesthesia is closely integrated with perioperative medicine to guarantee smooth progress of operations, comfortable recovery, and favorable long-term outcome for patients. There are a huge number of clinical data in anesthesia and perioperative medicine, and artificial intelligence (AI) has a powerful ability to analyze and evaluate data; thus, applying AI is a significant advantage in analysis and prediction based on real clinical big data in anesthesia and perioperative medicine. AI has made some progress in the field of anesthesiology and perioperative medicine. This review introduces the most encountered computerized techniques of AI in anesthesiology, main clinical applications themes of AI in anesthesiology, as well as limitations and ethical implications involved in deployment of this technology.