Volume 1, Issue 1

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

The Internet of Medical Things （IoMT） has given rise to a new medical paradigm: “Three links span all spatiotemporal dimensions, integrating four communities within. Quality control, preventive medicine, healthcare, and treatment together form a novel mode to benefit all human beings.” This paradigm facilitates medical alliances and hierarchical medical systems, enabling grass-roots doctors to better implement homogeneous healthcare practices with a strong foundation and extensive coverage, ultimately serving patients more effectively. However, in the real world, the implementation and advancement of this work still rely on experts’ promotion and implem entation. The most in-demand doctors have finite capacities, making it challenging to extend the influence of renowned physicians, serve patients on a larger scale, and benefit society more broadly. Now, with the development of the concept and related technology of metaverse, a new opportunity has arisen for the IoMT. This will help address the issue of limited capacity among top doctors by leveraging a virtual-real interaction platform and the eight major characteristics of the metaverse. Furthermore, it is possible to establish meta-hospitals thereby implementing a metaversal healthcare model that embodies “new opportunities in IoMT, direct encounters doctors in metaverse in medicine, quality control through virtual-real interaction, and unmatched integration of humans and machines.”By adopting a management model focused on “patient-centered care, emphasis on metaverse in medicine, specialized disease management, and quality assurance,” meta-hospitals can provide homogeneous healthcare that extends throughout and strengthens the primary care sector. Although there have been no recognized experiences with constructing metaverse hospitals so far, exploring and practicing this possibility while keeping in mind demand backgrounds, previous foundations, metaverse doctors, and management will undoubtedly serve as catalysts for further development. Constructing meta-hospitals contributes to the “Healthy China 2030” vision, adhering to its strategic theme of “building and sharing the health of all citizens.” 

Key Words: artificial intelligence; large model; metaverse in medicine; medical artificial intelligence

This paper discusses the needs and implementation of meta-hospitals from the perspectives of value, society, culture, and technology, and describes the basic architecture of meta-hospitals from the perspectives of integrating digital twins and digital natives. Based on the trend of technological development and a certain extension prediction, this paper attempts to semi-quantitatively analyze the feasibility of the infrastructure construction of the meta-hospital, and also emphasizes that the formation of the meta-hospital collaborative development system will be a long-term process. 

Key Words: meta-hospital; digital twin; digital native

This article reviews the development history and core technologies used in artificial intelligence （AI）, reviews the development history of large language models, summarizes the limitations and deficiencies of large language models, and identifies prospects for the development of general AI. This paper summarizes the performance strength and typical application scenarios of AI in the current medical field, analyzes the shortcomings of its application, and proposes the concept and classification of medical AI. On this basis, from the perspectives of medicine serving human beings and medicine’s own development, this commeutary defines the development goals of medical AI, and gives two different construction methods and paths for medical AI. 

Key Words: artificial intelligence; large model; metaverse in medicine; medical artificial intelligence

Metaverse in medicine forges a novel paradigm of digitized and intelligent healthcare services using AR/VR technology and the Internet of Medical Things (IoMT), transcending the spatial and temporal constraints of conventional medical practice and unveiling significant prospects in medical training, surgical support, and chronic disease management, among others. Concurrently, the ascent of metaverse in medicine poses challenges, including technology dependency, regulatory gaps, and a shortage of skilled professionals, particularly concerning data security and privacy protection. This article scrutinizes the regulatory demands and hurdles associated with digital medical devices, encompassing the oversight of hardware, software applications, and intelligent algorithms, and reviews regulatory strategies for digital health products in leading markets such as the FDA, MDR, and NMPA guidelines. In conclusion, the paper proffers strategic recommendations for businesses to navigate forthcoming regulatory obstacles, underscores the imperative of compliance management, and anticipates the influence of nascent technologies on the trajectory of regulatory evolution. 

Key Words: metaverse in medicine; digital intelligence medical equipment; regulatory policy; data security; personal privacy protection

Research on digital human GPT in medicine mainly focuses on its applications in healthcare. This technology can help doctors make diagnoses faster and more accurately by automatically interpreting medical images and electronic medical records, thereby improving diagnostic accuracy and efficiency. At the same time, it can provide personalized health education and patient care, which can improve the patient experience and increase patient satisfaction and compliance. In addition, GPT can automate the processing of large amounts of textual data, significantly reducing the workload of medical staff and reducing medical costs. Its prediagnosis and health management functions can also help detect and prevent diseases early, reducing the cost of later treatment. When applied in scientific research, GPT can identify anomalies in medical data and help researchers discover new treatments or disease prediction models. It can also automatically generate new hypotheses and protocols based on existing medical knowledge, providing practical recommendations for researchers. In addition, GPT can help solve medical problems and promote the progress of scientific research through reasoning and logical thinking. Looking forward to the future, digital human GPT in medicine has broad development prospects. With the continuous advancement of technology and the increasing demand for medical care, the application of GPT in the medical and health fields will be deeper and more extensive. It can not only improve the quality and efficiency of medical services but also promote innovation in and the development of medical research. At the same time, with the increasing demand for privacy and data security, we must understand how we can ensure the safe storage and processing of sensitive medical data, avoid the risk of data leakage, and maintain patient privacy and data compliance to ensure the future development of digital human GPT in medicine.

Key Words: digital human in medicine; GPT; natural language processing; medical; healthcare

Medical GPT, as a significant application of artificial intelligence technology in the healthcare field, has been explored in various areas, including medical imaging analysis, electronic medical record interpretation, disease prediction and diagnosis, and health management, demonstrating considerable potential for application. By using deep learning and natural language processing technologies, medical GPT can process and analyze vast amounts of medical literature and clinical data, thereby acquiring robust medical knowledge and reasoning capabilities. Current researches indicates that medical GPT has extensive application prospects in areas including intelligent diagnosis, health management, medical image analysis, drug research and optimization, and medical education and training. However, despite continuous technological advancements, the development of medical GPT still faces challenges in terms of data quality, privacy protection, security, and ethical regulations. Future development will require striking a balance between technological innovation and ethical regulations to ensure that medical GPT can evolve stably and healthily, bringing further innovation and value to the healthcare. 

Key Words: medical GPT; natural language processing; data security

Machine learning (ML) models often require large training datasets and lack the interpretability of latent variables. This novel delayed latent hybridization model (DLHM) incorporates piecewise-constant delays (PCDs) to model delays that are inevitably present in pharmacology and disease progression, a feature missing in existing approaches that leverage expert knowledge. By incorporating delays, we contributed a high-level expert knowledge in the design of dynamic systems modeling, which enhanced performance in predicting pharmacological and disease progression dynamics and aims to improve interpretability and communication to patients. Our findings indicate that DLHM demonstrates improved predictive reliability and congruence with the disease progression prediction task. The paper validates the model’s performance using synthetic data from COVID-19 patients, offering a significant advancement in biosciences modeling with delayed effects and expert knowledge. 

Key Words: machine learning; delayed latent hybridization model; piecewise-constant delays; disease progression prediction

Along with the research and application of metaverse in medicine, the development of consensus and guidelines has also been pushed to the agenda. However, since metaverse in medicine is a new thing, it is necessary to consider its particularity to formulate consensus and guidelines that are innovative, scientific, practical, and beneficial to the public. To achieve this, the following aspects need to be considered,(1) interdisciplinary integration: metaverse in medicine is a product of the cross-integration of medicine, information technology, artificial intelligence and other disciplines. Therefore, in the process of formulating consensus and guidelines for metaverse in medicine, it is necessary to absorb the opinions and suggestions of multidisciplinary experts to ensure their comprehensiveness and professionalism. This interdisciplinary integration makes the consensus and guidelines unique and complex in content. (2) Technological innovation: the core technologies that the metaverse in medicine focus on, such as virtual reality (VR), augmented reality (AR), mixed reality (MR), and extended reality (XR), are all innovative technologies that have developed rapidly in recent years, and their applications in the medical field are still in the exploratory stage. Therefore, consensus and guidelines need to pay attention to the latest technological trends and provide cutting-edge guidance for related research and practice. (3) Diversity of application scenarios: the metaverse in medicine covers a wide range of application scenarios, including medical education, preventive health care, clinical diagnosis, treatment intervention, and rehabilitation management, which makes it necessary to consider the actual needs and technical challenges in different scenarios in the process of compiling the consensus and guidelines, and provide targeted solutions and suggestions. (4) Regulatory and ethical requirements: since metaverse in medicine involves sensitive issues such as patient privacy and data security, it is necessary to fully consider the ethical requirements of relevant regulations when formulating consensus and guidelines. Consensus and guidelines need to clarify compliance requirements for data collection, processing, storage, and transmission to ensure patient privacy and data security. To sum up, the particularity of the consensus and guildlines for metaverse in medicine is mainly reflected in interdisciplinary integration, technological innovation, diversity of application scenarios, and regulatory and ethical requirements. These particularities make it necessary to be more cautious and comprehensive in the compilation process to ensure its scientific and practical nature.

Key Words: metaverse; metaverse in medicine; consensus; guideline 

Teaching rounds in metaverse represent an innovative method in medical education, combining metaverse technology with contemporary educational theories to provide medical students with a new learning platform. This teaching method allows students to immerse themselves in simulation clinical environments, thereby gaining a deeper understanding of patients’ conditions and treatment plans. During teaching rounds in metaverse, medical students can enter a simulated hospital environment through virtual reality technology, observe the conditions and symptoms of virtual patients, interact with them, conduct operations such as medical history collection and physical examination, and even participate in virtual surgeries. Meanwhile, teachers can provide real-time guidance and commentary using metaverse technology, helping students better master the relevant knowledge and skills. This teaching method has several advantages: (1)teaching rounds in metaverse allow students to experience clinical environments more realistically, thereby enhancing their interest in progressing and motivation to learn. (2)Virtual environments can simulate a variety of rare and complex cases, allowing students to be exposed to more clinical scenarios. (3)Metaverse technology can facilitate real-time assessment of and feedback on students’ operations, helping them promptly identify and correct mistakes. As technology continues to advance and application scenarios expand, metaverse teaching rounds will become a key feature in the development of medical education. 

Key Words: metaverse in medicine; internet of things; teaching rounds; virtual reality; augmented reality

The situation of tobacco control remains grim. Tobacco dependence is still a problem difficult to solve because of factors including unawareness of tobacco harm and reluctance to quit smoking among the public, poor treatment effects and compliance, and unmanageable smoking population, which leads to increasing health risk of nicotine dependence to smokers and even the whole society. Internet of Medical Things (IoMT) purses the aim “complex problems are simplified, simple problems are digitized, digital problems are programmed, and program problems are systematized”. Using QSapp 5A system, it intends to build medical model of smoking cessation with a strong community base and wide coverage, providing solutions to current problems. However, IoMT still cannot get rid of time and space limitation. Such intractable limit gives rise to metaverse in medicine in which virtual reality and augmented reality are combined with medicine. BRM tries to break through time and space limit by holographic construction, holographic simulation, virtual-real integration and virtual-real linkage, promoting tobacco control to an unprecedented level. 

Key Words: tobacco control; metaverse in medicine; internet of medical things

The metaverse in medicine is a cutting-edge conception in the field of medicine centered on technologies such as virtual reality, the Internet of Things, artificial intelligence, and digital twins, which seeks to resolve the dilemmas of the current situation of healthcare in China. However, the lack of legal regulation has objectively limited further development of metaverse in medicine. Starting from digital twins, a representative technology with great potential in this field, we discuss the institutional response and value reconstruction of related laws. The medical application of digital twins involves human rights, medical affairs and algorithms. How to improve informed consent mechanism, how to limit subject scope qualification, and how to clarify damage responsibility attribution together contribute the key issues in resolving the controversies. From the dispute resolution of digital twin medicine application, we can conclude an improvement approach of the overall legal regulation of metaverse in medicine. On the domestic organization mode, a stable legal interest protection core should be found to establish a new legal order of the algorithmic society. On the international organization mode, the public international law can play a coordinating and restraining function to establish a globalized medical metaverse. 

Key Words: metaverse in medicine; digital twin; legal regulation