Volume 2, Issue 2

Since the beginning of the 21st century, artificial intelligence (AI) has been profoundly reshaping medical research, propelling its transition from the traditional "hypothesis-verification" paradigm towards a “data-driven, generative” cognitive structure. Leveraging deep learning and generative pre-trained models, AI is not only transforming research workflows in areas such as literature review, image recognition, clinical trial design, and drug development, but also challenging the philosophical foundations, interpretability, ethical considerations, and evaluation mechanisms of medical research. This paper systematically analyzes the multifaceted evolution of AI’s role in medical research—from a tool to a collaborator, and from an accelerator to a paradigm architect. It proposes that a framework of “trustworthy, transparent, and controllable” AI should serve as the institutional cornerstone for reconstructing future research paradigms. By examining representative case studies and emerging trends under AI’s influence, the paper emphasizes that human-AI collaboration will become the new norm in medical knowledge production. It further calls for establishing interdisciplinary consensus mechanisms to ensure the harmonious progression of scientific rigor, ethical integrity, and innovative capacity in medical research.

Key Words: artificial intelligence; paradigm of medical research; data-driven science; generative pre-training model;  research ethics

The health of the elderly needs to be managed in four dimensions: physiological, psychological, functional and social, so as to delay decline, prevent diseases and increase longevity, and improve life satisfaction in a scientific way. However, the elderly in China face challenges such as chronic diseases, weak awareness of health management, lack of psychological services, and barriers to the use of digital tools, coupled with the increasing proportion of people living alone and empty nests, insufficient social support, and uneven medical resources, which further exacerbate health risks. New quality productivity empowers elderly care has become the key to breaking the situation, and technologies such as smart wearable and smart medical care can improve medical accessibility and management efficiency. AI robots alleviate loneliness, and digital technology enables precision health management. It can integrate long-term care insurance and community smart health care resources to form a new one-stop pension model. Intelligent monitoring and telemedicine, digital therapeutics, VR and other technologies can be applied to disease management, rehabilitation training, and optimization of treatment plans. Smart elderly care and home care combine AI nutritionists, blockchain and other technologies to provide personalized services. Data-driven can achieve precise health management, take into account privacy and security, and build closed-loop services. Community smart health care provides digital social and emotional care through intelligent environmental perception and AI health huts to optimize service quality. The expected effects include an increase in the accident recognition rate, a shortened response time, a decrease in the accident rate, and an improvement in self-care ability.

Key Words: health of the elderly; generative pre-training transformer; internet of things; artificial intelligence; augmented reality; virtual reality

Pulmonary function testing (PFT) is a critical tool for assessing respiratory health and managing diseases such as chronic obstructive pulmonary disease (COPD) and asthma. Leveraging its powerful natural language processing and big data analytics capabilities, Generative pre-trained transformer (GPT) can efficiently integrate multidimensional patient data to generate precise diagnostic reports and treatment recommendations, significantly enhancing interpretation efficiency and supporting clinical decision-making. Its applications encompass monitoring and early warning, comprehensive analysis, diagnostic assessment, and personalized treatment plan formulation. Particularly in managing chronic airway diseases like COPD and asthma, GPT provides individualized advice by tracking pulmonary function data, symptoms, and medication responses in real-time; it assists in dynamic assessment and prognosis prediction for interstitial lung diseases; and in the surgical domain, it supports the evaluation of operative tolerance and optimization of perioperative management. Furthermore, GPT can predict drug tolerance issues, offer suggestions for medication adjustments, and provide comprehensive health interventions incorporating lifestyle and environmental factors. This technology also extends remote medical services, mitigating disparities in healthcare resource distribution by enhancing service accessibility through data integration and conversion, intelligent analysis and reporting, and remote consultations. Implementation requires addressing challenges such as privacy protection, content accuracy, data preprocessing, user experience, and system stability. These can be addressed through measures like encryption technologies, constructing medical knowledge bases, developing data conversion modules, and optimizing interface design to ensure the secure and reliable application of GPT in pulmonary function testing.

Key Words: generative pre-trained transformer; artificial intelligence; chronic obstructive pulmonary diseases; asthma

GPT is demonstrating great potential in the medical field, especially in the diagnosis and treatment of colds. Its application not only improves the efficiency of diagnosis and treatment, but also enhances patient education and promotes the popularization and improvement of medical knowledge. In terms of cold diagnosis and treatment, GPT can automatically and intelligently analyze patients’ symptoms and quickly provide initial diagnostic suggestions, which is conducive to reducing the workload of doctors. Meanwhile, it can also generate easily understandable educational content to help patients gain a deeper understanding of their conditions, treatment plans and preventive measures. This personalized educational approach, combined with interactive learning and multi-channel dissemination, has greatly enhanced the health literacy of patients. GPT also plays an important role in the diagnosis and treatment of colds. Patients can obtain initial diagnostic suggestions by interacting with GPT to describe their symptoms, providing a strong reference for medical treatment. In addition, GPT can also recommend treatment plans based on the patient's condition, including medication, rest and dietary adjustments, etc. For patients with mild symptoms, GPT can also conduct remote monitoring, promptly alert them of changes in their condition, and provide management suggestions. However, the application of GPT in the medical field also faces challenges. Data privacy and security are the top priorities. It is essential to ensure the encryption and desensitization of patient data. Although GPT has certain application potential, its diagnostic accuracy still cannot be compared with that of experienced doctors. In addition, legal and ethical issues cannot be ignored. For instance, medical liability and informed consent of patients need to be further clarified. To address these challenges, it is necessary to enhance data protection, improve the diagnostic accuracy of the GPT model, and conduct reviews in combination with doctors’ experience. At the same time, relevant laws, regulations and ethical norms should be established and improved, key issues should be clarified, supervision and evaluation should be strengthened to ensure the compliant application of GPT in the medical field.

Key Words: generative pre-trained transformer GPT; artificial intelligence; cold

Acute respiratory distress syndrome (ARDS) is characterized by increased alveolar capillary permeability and hypoxemia, often leading to multi-organ failure. Early recognition and intervention, especially the optimization of mechanical ventilation, restricted volume control, treatment of anti-inflammation, and some new type of therapies, such as prone-position ventilation and ECMO, can significantly shorten ICU stays, reduce healthcare costs and improve patient survival. In order to optimize the diagnosis and treatment of ARDS, it is necessary to integrate research results, clinical guidelines and practical experience to build a systematic knowledge system. As a smart tool, GPT has shown great potential in the medical field. It can efficiently search medical databases, build knowledge graphs, develop online platforms, and provide personalized recommendations to help doctors quickly grasp the latest progress. At the same time, GPT can also generate high-quality education and training materials to meet the training needs of different medical staff. In the online training, GPT combines simulated cases and VR/AR technology to create an immersive learning environment and improve the diagnosis and treatment capabilities of grassroots doctors. GPT can also enable remote diagnosis and treatment, especially in low-resource settings, to accelerate the treatment process through remote diagnosis and assistance. In terms of clinical decision support, GPT can analyze electronic medical records, provide early warning and intervention recommendations, and customize personalized treatment plans. It also fosters multidisciplinary collaboration and technical exchange. In terms of resource allocation, GPT can analyze data and provide resource allocation suggestions for governments and medical institutions to optimize the allocation of medical resources. In remote areas, GPT serves as an online think tank, providing immediate guidance to grassroots doctors. However, the application of GPT also faces challenges such as data privacy, model accuracy, technical thresholds, imbalance of medical resources, and medical liability.

Key Words: generative pre-trained transformers; acute respiratory distress syndrome; extracorporeal membrane oxygenation

Orthodontics is a discipline that heavily relies on spatial perception and fine motor skills, placing extremely high demands on the practitioner’s experience, technique, and judgment. However, traditional teaching models face numerous challenges in orthodontic training. The metaverse, an emerging technology integrating virtual reality (VR), augmented reality (AR), artificial intelligence (AI), and other technologies, offers an immersive and highly interactive solution for orthodontic education. This article reviews the application and development of metaverse technology in orthodontic education.

Key Words: metaverse; orthodontics; education

This paper introduces BAIMGPT 1.0, an AI-powered medical metaverse platform, to democratize early diagnosis of obstructive sleep apnea (OSA) and pulmonary nodules. By integrating virtual care, federated learning, and IoT diagnostics, the platform addresses healthcare disparities in underserved regions while mitigating algorithmic bias and privacy risks through decentralized frameworks. BAIMGPT 1.0 exemplifies a scalable model for equitable chronic disease management, prioritizing inclusivity and ethical AI-metaverse synergy to redefine global healthcare delivery.

Key Words: BAIMGPT 1.0; obstructive sleep apnea; pulmonary nodules; metaverse

As the number one killer of cancer in the world, lung cancer causes about 2.1 million cases and more than 1.8 million deaths every year. AI technologies such as GPT have brought hope to the prevention and treatment of lung cancer, which can efficiently and accurately empower consultation and diagnosis and treatment suggestions through NLP, promote knowledge sharing and balanced improvement of medical standards, and provide patients with personalized health guidance and psychological comfort. The core technical principle is “four changes”. (1) Change the cleaning data to data selection; (2) Change simple consultation to face the digital expert; (3) Change blind favor to quality control and verification; (4) Change to simply evidence-based and increase medical experience. A controlled study between BAIMGPT and DeepSeek has shown significant advantages in terms of intimacy, security, question understanding, and answer accuracy. Its unique technical architecture and “four innovations” ensure professionalism and accuracy and provide an important reference for the future development of AI-assisted diagnosis systems. BAIMGPT can show significant value in key links such as lung cancer screening, pulmonary nodule consultation and management, diagnosis and staging, treatment plan formulation and postoperative management. Through intelligent image analysis, the diagnosis and treatment process can be optimized, medical costs can be reduced, and resource utilization efficiency can be improved. The successful implementation of BAIMGPT relies on the collaborative work of medical experts, information engineers, data analysis experts and other talents in multiple fields to jointly build a highly applicable and easy-to-operate knowledge system. Through the integration of interdisciplinary knowledge, the efficiency and accuracy of lung cancer screening and evaluation are improved, and the patient consultation experience is optimized. User experience optimization: BAIMGPT has been comprehensively optimized in terms of interface design, intimacy, sense of security, visual empowerment, voice interaction, accessibility and convenience, etc., to ensure that users can quickly get started and generate high-quality reports, enhance patient confidence, and optimize the diagnosis and treatment experience. BAIMGPT has been granted a registered trademark by the State Intellectual Property Office and approved by the Ethics Committee of Zhongshan Hospital. Cooperate with the International Metaverse Medical Association and the China Lung Cancer Prevention and Control Alliance to ensure the legitimacy and professionalism of the technology. At the same time, BAIMGPT strictly adheres to ethical requirements, reduces human bias, and ensures the fairness and transparency of the operation of the system. With the rapid development of AI technology, BAIMGPT will show potential in the field of lung cancer screening, evaluation and diagnosis and treatment. In the future, the efficiency and accuracy of the system can be further improved by combining other imaging technologies, optimizing the accuracy of disease-specific models, and expanding the functions of disease progression prediction. BAIMGPT is expected to become an important force in promoting the development of public health and helping to achieve the important goal of “Healthy China”.

Key Words: artificial intelligence; generative pre-trained transformer; natural language processing; lung cancer screening