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Background: Recently, Professor Chunxue Bai and colleagues have proposed a definition of the Metaverse in Medicine as the medical Internet of Things (MIoT) facilitated using AR and/or VR glasses. Methods: A multi-disciplinary panel of doctors and IT experts from Asia, the United States, and Europe analyzed published articles regarding expert consensus on the Medical Internet of Things, with reference to study results in the field of metaverse technology. Findings: It is feasible to implement the three basic functions of the MIoT, namely, comprehensive perception, reliable transmission, and intelligent processing, by applying a metaverse platform, which is composed of AR and VR glasses and the MIoT system, and integrated with the technologies of holographic construction, holographic emulation, virtuality-reality integration, and virtuality-reality interconnection. In other words, through interactions between virtual and real cloud experts and terminal doctors, we will be able to carry out medical education, science popularization, consultation, graded diagnosis and treatment, clinical research, and even comprehensive healthcare in the metaverse. The interaction between virtual and real cloud experts and terminal users (including terminal doctors, patients, and even their family members) could also facilitate different medical services, such as disease prevention, healthcare, physical examination, diagnosis and treatment of diseases, rehabilitation, management of chronic diseases, in-home care, first aid, outpatient attendance, consultation, etc. In addition, it is noteworthy that security is a prerequisite for the Metaverse in Medicine, and a reliable security system is the foundation to ensure the normal operation of such a platform. [ABSTRACT FROM AUTHOR]

The emergence of the Medical Internet of Things (MIoT) has presented an opportune solution to these problems, as it provides effective technological support. Based on the current MIoT theory, it is feasible to realize efficient and accurate graded diagnosis and treatment through the linkage between doctors in large hospitals (the 'Cloud Experts') and doctors in small hospitals (the 'Terminal Doctors'), and to contribute to the research and development of related technologies to improve graded diagnosis and treatment in China. Nevertheless, the following issues remain in clinical practice: (1) The Cloud Experts are not available to dedicate themselves to popular science education, or to give professional lectures, at all times and in all settings. (2) The Cloud Experts are not available to provide guidance for the Terminal Doctors on diagnosis and treatment at all times and in all settings. (3) In clinical trials, the main researchers are not available to monitor the research or guide the team at all times and in all settings. (4) Due to the lack of real-time quality control at all times and in all settings, non-standard diagnosis and treatment, with the features of a handicraft workshop, still exist to a considerable degree. The real cause lies in the limitations of the Internet technology itself, which cannot facilitate communication at all times and in all settings between the Cloud Experts and the Terminal Doctors involved in graded diagnosis and treatment. Therefore, the MIoT-based digital platforms need to be further improved, especially concerning the communication and interactions between humans and computers, and the integration and linkage between the virtual and real worlds. It is gratifying that the concept of the metaverse has been introduced, which provides a possible solution to all these problems, and serves as a foundation for the proposal and development of the Metaverse in Medicine. [ABSTRACT FROM AUTHOR]

The metaverse has entered people's horizons through virtual reality, digital twinning, the Internet of Things, blockchain technology, etc. In the current healthcare system, the management of chronic diseases, such as chronic obstructive pulmonary disease (COPD) and obstructive sleep apnea-hypopnea syndrome (OSAHS), still faces challenges, such as uneven distribution of medical resources, and difficulty in follow-up, overburdening of specialists, and so on. However, metaverse medical platforms incorporating advanced AI technologies, such as industrial-scale digital twins, may address these issues. In this article, we discuss the application prospect of these technologies in digital medicine and the future of the medical metaverse. [ABSTRACT FROM AUTHOR]

Chronic respiratory diseases are staggering health burdens affecting the lives of hundreds of millions of people around the world, contributing substantially to mortality and morbidity globally, including in the United States, China and Europe. Chronic obstructive pulmonary disease (COPD), lung cancer, pneumonia, interstitial lung disease (ILD) and asthma are among the leading diseases that urges the development of effective prevention, diagnosis, treatment and management of these diseases. Medical Internet-of-Things (MIOT) is fast becoming one of the most promising approaches to achieve this largely due to its cost effectiveness, non-invasive deployments and automation capabilities. Coupled together with strategically leveraging artificial intelligence, continuous data collection and real time monitoring and response systems, MIOT shows potential to be an effective and efficient solution. [ABSTRACT FROM AUTHOR]

Background: Early diagnosis and treatment are key to improving survival and prognosis of patients with lung cancer. Liquid biopsy and artificial intelligence (AI) can help diagnose early lung cancer. Case presentation: We present a 68-year-old female with a family history of pulmonary nodules without smoking history who found multiple nodules in the lungs on physical examination. Malignant lung nodules were diagnosed based on AI evaluation and 341 circulating abnormal cells (CACs). Surgical resection confirmed the diagnosis of pulmonary adenocarcinoma and the CACs decreased to 151 at postoperative follow-up in one month and to 54 six months after surgery. Conclusion: CACs helps to improve the accuracy of AI diagnosis of early lung cancer and the prognosis of patients with lung cancer at follow-up. [ABSTRACT FROM AUTHOR]