Obstructive sleep apnea (OSA) diagnosis and treatment simulation training system is a kind of training mode and training system, which combines cloud technology and terminal equipment to simulate the actual diagnosis and treatment scene, allow remote experts to teach doctors the diagnosis and treatment of OSA, and to improve the level of OSA diagnosis and treatment. This article lists the points for attention in the simulation training of diagnosis and treatment of OSA in cloud plus terminal, which can be used for reference for clinical practice.

Key Words: obstructive sleep apnea; artificial intelligence; internet of things; metaverse

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

With the continuous development of metaverse technology, its application in the traditional Chinese medicine (TCM) will be more and more extensive. In order to ensure the correct application of metaverse technology and guarantee the safety and interests of patients, it is necessary to formulate corresponding consensus and guidelines to regulate the use of metaverse technology. In this paper, the methods of making consensus and guideline of metaverse technology in TCM diagnosis and treatment are summarized.

Key Words: consensus and guideline; metaverse technology; traditional Chinese medicine

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

The aging population is accompanied by a decline in human body function, leading to an increasing number of people with lower limb dysfunction, which has become a global public health challenge today. The lower limb rehabilitation exoskeleton robot based on surface electromyography is a current research hotspot. It can help people with lower extremity dysfunction perform better rehabilitation training. This review presents the analysis and processing of surface electromyography, feature extraction and recognition, as well as the control methods for lower limb rehabilitation exoskeleton robots.

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

With the rapid development of Virtual Reality (VR) technology, its widespread applications in entertainment, education, healthcare, and other fields have brought significant innovations and conveniences to society. However, as the technology becomes more prevalent, ethical risks related to physical and mental health have also become increasingly evident, particularly in the context of prolonged use and high-immersion experiences. This paper constructs a multi-dimensional ethical risk framework for VR technology, addressing risks at the personal level (physical and mental health risks), the individual-social interaction level (social indifference and lack of virtual community responsibility), and the societal-institutional level (privacy protection and intellectual property issues). Based on these risks, this paper proposes a systematic approach to ethical governance, including a multi-stakeholder collaborative governance model, ethical awareness enhancement through education and empowerment, and the mutual promotion of technological innovation and ethical constraints. By fostering continuous collaboration among government, tech companies, psychological research institutions, and educational departments, and incorporating user feedback and participation, the paper advocates for the construction of an open, transparent, and inclusive human-centered technological ecosystem. Ultimately, this approach aims to ensure the sustainable development of VR technology while balancing individual and societal well-being.

Key Words: virtual reality; ethical risks; technological governance; personal well-being; collaborative governance

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