Volume 2, Issue 1

In order to cope with the challenges and inherent limitations in the development of medical GPT technology, the author suggests a comprehensive and in-depth innovation strategy, which requires a refined reshaping of every link from data acquisition to system operation and maintenance. Data collection is no longer just a quantitative accumulation but a qualitative leap, which means carefully selecting from a vast amount of medical information to ensure that each piece of data is highly representative, accurate, and usable. This process requires not only the support of advanced technical means, but also the in-depth participation of medical experts to achieve accurate data screening and value mining. In the selection of the pedestal model, the traditional simple question and answer framework should be abandoned, and the possibility of building an expert digital human doppelganger should be explored. This transformation allows patients to receive more personalized and professional medical consultation services as if they were directly facing experienced doctors, which greatly improves the interactive experience and trust. At the same time, in order to ensure the security and accuracy of medical information, it is recommended to apply an AI-based intelligent quality control mechanism to replace the blind reliance in the past, and strictly control the quality through a combination of automatic review by algorithm and manual review. In addition, the training, evaluation and optimization of models should also pay more attention to the integration of practical experience. On the basis of evidence-based medicine, it advocates the integration of the clinical wisdom and experience of big doctors into the model, so that medical GPT technology can not only provide patients with more accurate and individualized diagnosis and treatment suggestions based on the latest scientific research results, but also combine with clinical practice. In short, it is necessary to realize the four major transformations from data cleaning to selection, from simple consultation to expert clone, from blind trust to quality control, and from simple evidence-based to combined with the experience of doctors.

Key Words: artificial intelligence; generative pretrained transformer; medical generative pretrained transformer; natural language processing; open evidence

The center for new-quality productive forces in medicine is an institution dedicated to driving innovation and development in the medical field. The center will adopt cutting-edge new qualitative productive forces technologies and methods, such as artificial intelligence, the Internet of Things in medicine, metaverse in medicine and digital human medical GPT, metaverse technology and Internet of Things technology, while integrating new-quality productive forces quality control system, streamlining processes, strengthening supervision, strengthening information management and effect evaluation, and realizing the linkage between patients, general practitioners and medical center experts to improve the quality and efficiency of medical services. In addition, the center will focus on the cultivation and use of virtual and real talents, the management of medical platforms (including clinics and wards), and the management of social and economic efficiency. The vision of this center is to provide a homogeneous medical service platform with patient-centered, meta-medicine as the focus, special diseases as the starting point, and quality control as the guarantee, as well as a corresponding new-quality productive forces medical model, to help achieve the goal of the Healthy China 2030 plan with strong grassroots and wide coverage.

Key Words: artificial intelligence; Internet of Things in medicine; metaverse in medicine; digital human medical GPT; new quality productive forces

The Internet of Things, Metaverse and Medical GPT are used to empower the field of health care, and the quality and efficiency of health care are improved through high-tech means. IoT technology provides a scientific basis for developing personalized health plans by deploying sensors and monitoring devices to monitor individual health status in real-time and collect data. The metaverse builds a three-dimensional virtual space, allowing users to participate in a variety of health promotion activities as virtual identities and enjoy immersive health education experiences, such as exercise training and rehabilitation courses in virtual reality. Medical GPT uses natural language processing technology, combined with the user’s genetic, physiological indicators and other information, to provide personalized health consultation and prevention strategies, generate customized health management plans, assess health risks, and provide professional consultation and education. The combination of these technologies not only improves the personalized and intelligent level of health management, but also expands the accessibility of health education resources, so that residents in remote areas can also enjoy high-quality resources. In addition, they reduce reliance on traditional healthcare resources, reduce costs, improve service efficiency, and enhance user engagement and satisfaction with health promotion activities by providing interactive and fun virtual reality experiences. These technologies are conducive to promoting innovation in medical and health services, promoting the equitable distribution of health resources, and improving the health level of the whole people.

Key Words: virtual reality; augmented reality; Internet of Things; generative pretrained transformer

The new quality productivity empowers chronic respiratory disease health tourism, that is, the use of modern technology and innovative means to enhance the travel experience and quality of life of patients. Specifically, it includes: improving the telemedicine system, monitoring and diagnosing patients’ health in real time, and ensuring timely medical assistance; applying smart devices to track physiological indicators and adjust treatment plans; developing personalized health plans, designing travel programs that are suitable for patients, and reduce the risk of disease; providing psychological support to help cope with travel challenges; the government guides the integration of resources and encourages the participation of all sectors of society to form a win-win situation; promote interdisciplinary collaboration and research and development of comprehensive service solutions; strengthen the training of tourism personnel and improve the care capacity; regular monitoring and evaluation to optimize the service content to ensure the best travel experience. These measures will provide a dual guarantee for the travel and health of patients.

Key Words: artificial intelligence; Internet of Things in medicine; metaverse in medicine; medical GPT; new quality productive forces

As a Chinese indigenous general-purpose large language model, DeepSeek is transforming the medical field with its efficient, low-cost training and reasoning, and localization advantages. This paper explores DeepSeek’s application possibilities in medicine, covering its uses in diagnosis, treatment, data management, patient services, resource optimization, and gene - biotechnology. It also outlines four levels of DeepSeek adoption by medical institutions, addresses challenges like superficial application, user privacy risks, and model hallucinations, and suggests solutions. Looking ahead, as medical knowledge bases grow and model interpretability improves, DeepSeek is set to boost precision medicine and intelligent decision-making, becoming a key driver of medical productivity.

Key Words: DeepSeek; large language model; medicine; enterprise applications

Artificial intelligence (AI) is increasingly utilized in precision medicine, with notable applications observed in neuropathology. In glioma diagnostics, histological classification, molecular subtyping, and WHO grading are automated by AI-based platforms, enhancing diagnostic consistency and operational efficiency. Critically, AI predicts prognosis, assesses survival and recurrence risks, and guides personalized treatment strategies. As issues like data silos and “black-box” algorithms are resolved, AI is poised to support decision-making by pathologists and clinicians throughout the clinical workflow of glioma management.

Key Words: glioma; artificial intelligence; neural networks; prognosis

With the rapid development of artificial intelligence technology, digital humans based on language models, such as GPT, have been widely applied in medical education. Digital human GPTs not only assist in learning medical knowledge and training clinical skills but also provide students with real clinical experiences through virtual patient simulations. Despite technical and ethical challenges, the personalized teaching and interactivity of digital human GPTs undoubtedly bring innovative changes to medical education. This article reviews the current literature to explore the application status, technological advancements, research findings, and challenges faced by digital human GPTs in the field of medical education， and looks forward to the future development directions of digital human GPTs in medical education.

Key Words: digital human GPT; medicine education

This paper discusses the application of advanced artificial intelligence technology in the field of medicine, with special attention to the innovative application of reinforcement learning and distillation technology and the legal compliance issues that need attention in the application. Using the latest large language model (LLM) technology combined with reinforcement learning and distillation technology developments as examples, the key issues of AI technology intellectual property boundaries, healthcare data use compliance, and healthcare AI regulatory framework are analyzed. This paper discusses how to promote the innovation of medical AI while ensuring the protection of patients’ rights and interests and compliance with medical ethics, and provides a theoretical reference for the healthy development of medical AI.

Key Words: reinforcement learning; distillation; artificial intelligence; medical GPT