Volume 1, Issue 3

Medical new quality productive forces (MNQPF) is a relatively new concept that refers to the ability to apply modern technological tools, especially emerging technologies like the internet, big data, artificial intelligence, and the metaverse, to improve the quality and efficiency of medical services. The core of this ability lies in innovation, including technological, managerial, and service innovation. This article reviews the technical foundations and current development status of MNQPF, points out the direction for its development, and aims to contribute to the construction of a modern medical system and the realization of the Chinese Dream of national rejuvenation.

Key Words: new quality productive forces in medicine; artificial intelligence; internet of thing

The development and progress of digital technology is driving traditional medicine towards metaverse in medicine. New quality productive forces is generated by revolutionary technological breakthroughs, innovative allocation of production factors, and deep industrial transformation and upgrading. It features high technology, high efficiency, and high quality. Metaverse in medicine is both an important component of new quality production forces, and will promote the new quality productive forces to a higher level by improving health level of workers. It will also promote the formation and role of new quality productive forces on a larger scale and to a greater extent. To this end, we must consciously follow the guidance of the theory of new quality productive forces, accelerate the digitization of medical knowledge, utilize artificial intelligence to generate new medical knowledge, and bravely apply metaverse in medicine to broader fields, so as to maximize the improvement of new quality productive forces level, and promote the formation and operation of new quality productive forces.

Key Words: metaverse in medicine; new quality productive forces; workers; artificial intelligence; digital technology

Health management aims to prevent diseases and improve quality of life by comprehensively monitoring, analyzing, and evaluating the health status of individuals or groups. The application of metaverse technology can further enhance the comprehensiveness, accuracy, interactivity, innovation, and personalization of health management, providing better health protection for human. However, it is also necessary to note that metaverse technology may have problems such as data privacy and technical thresholds during application, which require corresponding measures to prevent and solve.

Key Words: health management; virtual reality; augmented reality; artificial intelligence; internet of things; metaverse in medicine

Obstructive sleep apnea (OSA), a common sleep-related disorder with a high prevalence and significant burden, has attracted widespread attention. Currently, OSA management faces several challenges: lack of professional diagnostic equipment, insufficient expertise among primary care physicians, uneven distribution of medical resources, and low awareness of the disease. To further address these challenges, we need to adopt new quality productive forces and establish a virtual OSA platform. This platform will integrate advanced medical technology and big data analysis to overcome limitations in professional knowledge and availability of specific equipment, thereby providing patients with more accurate and personalized diagnosis and treatment plans.

Key Words: obstructive sleep apnea; internet of things; metaverse; virtual reality; augmented reality

In the era of digital economy, the new quality productive forces, supported by digital, networked and intelligent new technologies, with scientific and technological innovation as the core driving force, has a wide range of penetration and integration, and is profoundly changing the development mode of all walks of life. Nebulizer therapy is an effective way to treat respiratory diseases such as asthma and chronic obstructive pulmonary disease by converting liquid drugs into tiny particles and delivering them directly to the lungs. The application of new quality productive forces, such as the Internet of Things, artificial intelligence, and the metaverse, has revolutionized nebulizer therapy. These technologies not only enable real-time monitoring and accurate analysis of patient physiological data to support the development of personalized treatment plans, but also improve the convenience of treatment and patient compliance. Through IoT technology, healthcare professionals can remotely monitor the treatment process to ensure the safety and effectiveness of treatment. At the same time, the introduction of AI technology has improved the efficiency of data-driven decision-making, making treatment plans more precise and scientific. However, new quality productive forces enabling nebulizing therapy also face challenges such as technology acceptance, data security and privacy protection, and economic cost. In the future, with the continuous development and improvement of technology, new quality productive forces will play a greater role in the field of nebulizer therapy and promote the high-quality development of medical service system.

Key Words: medical new quality productive forces; nebulizer therapy; metaverse in medicine

Primary hospitals play a crucial role in early screening and diagnosis of lung cancer. This not only significantly improves the survival and cure rates of patients, but also greatly enhances their quality of life, reduces the burden on the healthcare system, optimizes resource allocation, and promotes the synchronous development of related medical industries and the economy. However, primary hospitals still face multiple challenges in lung cancer screening and early diagnosis. Issues such as outdated equipment and technology, uneven levels of personnel, unequal distribution of resources, lack of patient awareness, and insufficient policy support are particularly prominent. To overcome these challenges, we need to address them from multiple dimensions, including updating medical equipment, enhancing personnel training, optimizing resource allocation, improving patient education levels, and seeking more policy support.

Key Words: pulmonary nodules; lung cancer; primary hospital; screening; diagnosis; artificial intelligence

Addressing the issue of resource scarcity for named entity recognition tasks in the medical field, a unified annotation methodology for special diseases entity corpora was formulated under the guidance of medical experts, and two special diseases entity corpora were constructed, namely Pediatric Bronchopneumonia Entity Corpus and Diabetes Entity Corpus. To verify the effectiveness of the proposed special disease entity corpus annotation method, the Pediatric Bronchopneumonia Entity Corpus was first compared with the publicly available dataset using BERT-BiLSTM-CRF and ERNIE-BiLSTM-CRF models. Then, the methodology was reapplied to diabetes electronic medical records to evaluate the robustness of the model. The results showed that both special diseases entity corpora got higher F1 scores than the public datasets, which suggests that special diseases entity corpus annotation methodology proposed in this paper has good robustness.

Key Words: electronic medical record; named entity recognition; corpus construction; Pediatric Bronchopneumonia Entity Corpus; Diabetes Entity Corpus

Modern medicine’s comprehensive management of pulmonary nodules primarily focuses on imaging screening, pathological diagnosis, and intervention for nodules with confirmed malignant pathology. However, there is a lack of effective treatment interventions for populations with insufficient imaging diagnostic evidence or who do not currently meet the indications for pathological biopsy. Integrating artificial intelligence, particularly GPT, into healthcare can revolutionize patient management by providing continuous personalized support. This real-world based research proposal aims to assess whether GPT-based consultations can improve pulmonary nodule management and patient satisfaction compared to traditional consultations. By placing artificial intelligence in the context of early lung cancer screening, this study hopes to fill the current gaps in pulmonary nodule management practices and offer scalable personalized solutions.

Key Words: pulmonary nodules; generative pre-trained transformer; real-world research

Designing real-world studies based on the clinical application of medical generative pre-trained transformer （MGPT) requires careful consideration and detailed planning of the research process. Compared to traditional clinical studies, such studies involve not only the evaluation of technology but also considerations of healthcare service efficiency, medical costs, and other aspects. This article elaborates on the design scheme of real-world studies on the clinical application of MGPT to ensure the high quality and reliability of the research, providing a solid evidence base for the application of artificial intelligence in the medical field and making a positive contribution to driving continuous progress and innovation in the entire healthcare industry.

Key Words: medical generative pre-trained transformer; artificial intellingence; real-world study