Cancer poses a severe threat to human health, and its early and rapid diagnosis can significantly improve patient prognosis. Raman spectroscopy enables the rapid and accurate detection of biochemical changes in cancer tissues at the molecular level, offering unique advantages in cancer diagnosis. However, Raman spectral data are large-scale and complex, making direct analysis by researchers infeasible. Artificial intelligence algorithms, with their rapid and precise data processing and analysis capabilities, provide an effective solution to this challenge. Studies have shown that combining Raman spectroscopy with artificial intelligence has significantly improved diagnostic accuracy and efficiency, while facilitating the discovery of novel tumor biomarkers. This article reviewed the current application status of artificial intelligence-assisted Raman spectroscopy technology in the field of cancer diagnosis, systematically expounded its technical principles and core advantages, deeply analyzed the key challenges currently faced, and looked forward to the future development trends, aiming to provide references for promoting the clinical transformation and application development of intelligent Raman spectroscopy technology in the rapid, accurate, early and non-invasive diagnosis of cancer.

Breast cancer-related lymphedema is a high-incidence complication following breast cancer surgery that severely impacts patients’ daily lives. Complete decongestive therapy (CDT) is currently the “gold standard” for lymphedema treatment, and pressure therapy is one of the cornerstones of CDT. Among pressure therapy devices, pressure sleeves, as wearable medical devices, offer advantages of portability, non-invasiveness, and continuous pressure control, making them an important tool for home-based management of lymphedema. In recent years, with the emergence of various novel foreign pressure sleeves for lymphedema, new developments have been witnessed in lymphedema treatment. This paper reviewed the research status of foreign novel lymphedema pressure sleeves in terms of design, materials, functions, therapeutic mechanisms, and clinical applications, analyzed and concluded their characteristics, aiming to provide references for the research and development of lymphedema pressure therapy devices in China, and promote the synergistic improvement of lymphedema treatment efficacy and patient adherence.

With the continuous promotion and deepening of hospital informatization management construction in China, the application of information technology to achieve intelligent management of the central sterile supply department (CSSD) has become an inevitable trend in the development of hospital CSSDs. The CSSD information-based quality traceability system is an intelligent system that can record and track the entire process of reusable instruments, devices, and items from CSSD processing to clinical department use. Through the application of this traceability system, dynamic monitoring and accurate tracking of all links in the disinfection and sterilization process can be realized, with responsibilities assigned to specific individuals. This not only enhances the sense of responsibility among staff but also ensures the safety of sterile supply, making it a key component of quality control management in hospital CSSDs. Based on a literature review, this article elaborated on the current development status of informationbased quality traceability systems in CSSDs, and specifically analyzed their advantages and shortcomings during application, aiming to provide new insights for the development of more professional and intelligent CSSD quality traceability management systems, further improve the quality of hospital instrument management, and promote the construction of intelligent CSSDs in hospitals.

With the rapid development of information technology, digital medical devices are profoundly transforming traditional medical models by virtue of their advantages in real-time monitoring, aided diagnosis, and data-driven decision-making. However, existing literature has mostly focused on single devices or specific application scenarios, lacking systematic integration and indepth analysis of their technical implementation paths and clinical application processes. This review summarized the core concepts and technical frameworks of digital medical devices, systematically sorted out key implementation approaches such as sensors, algorithms, and wireless communication, and concluded the typical application processes and practical cases of wearable devices and implantable devices in remote monitoring and smart healthcare. From the perspectives of standardized interconnection, data privacy and security, operational usability, and doctor-patient acceptance, this paper conducted an in-depth analysis of the main challenges encountered in the current promotion process and looked forward to the future development trends of digital medical devices, aiming to reveal the coupling mechanism between technologies and application scenarios, thereby providing theoretical and practical references for device R&D, standard formulation, and clinical implementation.

Single-photon emission computed tomography/computed tomography (SPECT/CT) is a crucial modality for detecting myocardial perfusion in patients at rest and under stress. It can accurately assess myocardial ischemia in patients with coronary artery disease and exhibits excellent sensitivity and specificity, thus holding significant value in the diagnosis, risk stratification, and prognosis assessment of coronary artery disease. In recent years, SPECT/CT technology has achieved rapid advancement, and its application in the diagnosis of coronary artery disease-related myocardial ischemia has become increasingly widespread with universal recognition. However, insufficient understanding among some medical professionals has hindered its further development. This review summarized the main types and characteristics of SPECT/CT radiotracers, the progress and optimization of imaging methods, as well as their applications in the diagnosis of coronary artery disease-related myocardial ischemia. Additionally, it provided an outlook on future development directions, aiming to enhance medical professionals’ understanding of this field and promote the broader application of SPECT/CT in the management of coronary artery disease-related myocardial ischemia.

Raman spectroscopy is a molecular spectroscopic analysis technique based on the interaction between light and matter. In recent years, with the assistance of artificial intelligence, the information contained in Raman spectroscopy data has been effectively and in-depth mined, thereby achieving significant progress in the non-invasive diagnosis of cancer. The application of Raman spectroscopy overcomes some limitations of traditional diagnostic methods, such as high invasiveness, long detection cycle, and low sensitivity in the early stage of cancer. With the development of nanomaterials, microfluidic technology, and multimodal data fusion technology, Raman spectroscopy exhibits high accuracy and specificity in cancer detection, and demonstrates great potential especially in early cancer screening and health monitoring. This paper reviewed the current application status of Raman spectroscopy in the detection of noninvasive samples (including urine, saliva, exhaled gas, and sweat), aims to promote the application of Raman spectroscopy in fields such as clinical diagnosis, disease monitoring, and health management.

Brain tumors are lesions caused by the abnormal proliferation of brain tissue cells, posing a significant threat to human life. Magnetic resonance imaging (MRI) is a typical non-invasive imaging technique that can generate high-resolution, non-destructive brain images without skull artifacts. With the continuous advancement of biomedical technology, the application of MRI in the diagnosis and treatment of brain tumors has become a key technical means to improve patient survival rates and reduce computational costs. However, there are still technical bottlenecks in the accurate segmentation of brain tumors using multimodal MRI. Traditional methods rely on manually designed features, which are not only inefficient but also susceptible to subjective influences. In contrast, deep learning has overcome this limitation through its capability of automatic feature extraction, thereby promoting the automation and precision of segmentation. This paper reviewed the technical principles, application effects, and limitations of three types of deep learning models, namely U-Net, Transformer, and segment anything model. Finally, it analyzed the challenges faced by current research and provided an outlook on future directions, aims to provide references for researchers and clinicians in the field of medical image analysis, and further enhance the accuracy and efficiency of brain tumor diagnosis.

Mild cognitive impairment (MCI) is a transitional state characterized by mild cognitive decline, serving as a precursor stage to Alzheimer’s disease (AD). It plays critical importance in the early diagnosis and prevention of AD. As a significant tool for investigating brain functional connectivity (FC), resting-state functional magnetic resonance imaging (rs-fMRI) plays a crucial role in understanding FC changes across different subtypes of MCI. This paper reviewed the application progress of rs-fMRI in revealing brain FC changes in patients with amnestic MCI (aMCI) and non-amnestic MCI (naMCI). It summarized the existing differences in FC within key brain networks, including the default mode network, executive control network, and salience network, between aMCI and naMCI patients, these differences may serve as biomarkers for the early diagnosis of MCI. This paper also discussed the clinical challenges in MCI diagnosisand how rs-fMRI can be utilized to enhance diagnostic accuracy while unraveling its underlying neurobiological mechanisms, aiming to provide neuroimaging evidence for the precision diagnosis and treatment pathway of MCI.

With the development of digital and information technologies, medical imaging has evolved from electronization and networking toward the direction of big data and intelligence. Generative medical imaging is capable of generating high-resolution images of different modalities and enabling cross-modal medical image conversion. Along with the advancement of artificial intelligence (AI), generative medical imaging is expected to play a significant role in all aspects of medical imaging. This paper reviewed the development stages of digitalization and informatization of imaging data, the application modes of AI in digital imaging, and the applications of generative medical imaging. Meanwhile, it explored the entire clinical workflow of generative medical imaging as well as the problems and difficulties in data management, analyzed the future development directions and challenges faced by generative medical imaging, and emphasized the importance and urgency of standardized management for generative imaging data, aiming to promote the management of generative AI-medical imaging data so as to meet the needs of clinical diagnosis.

Disorders of consciousness (DOC) are a group of severe neurological disorders. Accurate assessment of the severity of DOC and related pathophysiological mechanisms is crucial for patients’ treatment and prognosis. Traditional imaging examinations, such as conventional magnetic resonance imaging or computed tomography, have limitations in evaluating microstructural changes and brain functional connectivity in DOC patients. Magnetic resonance 3D-T1 weighted imaging (3D-T1WI) and diffusion tensor imaging (DTI) can objectively reflect abnormal brain structural and functional changes, provide detailed imaging evidence for exploring the pathophysiological mechanisms of DOC, and possess unique advantages in the field of DOC assessment. This paper reviewed the latest applications of 3D-T1WI and DTI in DOC assessment, summarized the changes in brain structure and function as well as their pathophysiological mechanisms in DOC patients, and aiming to provide a reference for the diagnosis, prognosis, and treatment of DOC.

Patients with lower limb motor dysfunction face severe limitations in motor function. Partial weight-bearing training, as an effective rehabilitation method, has attracted considerable attention for its ability to specifically help patients gradually restore mobility. However, traditional partial weight-bearing rehabilitation training has limitations such as reliance on physicians’ experience and poor patient compliance. The lower limb body weight-supported walking device has effectively broken through the limitation of high dependence on physicians in traditional rehabilitation methods, providing patients with a more independent and efficient rehabilitation training platform. This paper summarized the core concepts and development history of lower limb body weightsupported walking devices. Based on the latest research results, it classified the mainstream weight-bearing reduction devices on the market and comprehensively analyzed the unique advantages and potential shortcomings of each type of device, aiming to point out the direction for future research and development as well as application, and promote the development of lower limb rehabilitation technology towards intelligent and personalized directions.

Regular blood pressure monitoring is of great significance for populations at high risk of hypertension. Existing contactbased detection technologies are associated with invasiveness and are not applicable to special groups such as patients with burns and scalds, thus creating an urgent demand for non-contact detection methods. Biomedical radar enables non-contact detection of subtle movements on the body surface to extract pulse waves for blood pressure calculation. Non-contact blood pressure detection technologies based on biomedical radar are mainly divided into three categories: those based on pulse wave velocity, those based on multiple characteristic parameters of pulse waves, and those based on deep learning. This paper reviewed the principle of noncontact blood pressure detection using biomedical radar and the research progress of these three core technologies. It also analyzed the shortcomings of each technology in terms of adaptation to individual differences, data coverage, real-time performance, and other aspects. The aim is to identify technical bottlenecks, point out future development directions, provide references for the clinical transformation of this technology, and help improve the convenience and universality of blood pressure monitoring.

As a crucial tool for optimizing end-of-life medical decision-making, advance care planning (ACP) has garnered widespread attention from healthcare systems worldwide. By proactively communicating patients’ medical preferences with the patients themselves and their families, ACP ensures that medical decisions align with individuals’ wishes, reduces unnecessary overtreatment, and enhances the quality of life during the end-of-life stage. In recent years, electronic health record (EHR) systems have been integrated into ACP to provide standardized functions for information identification, storage, sharing, and reminders. This integration aims to increase the implementation rate of ACP and improve the quality of end-of-life care. Currently, the promotion of ACP, both domestically and internationally, still faces numerous challenges, such as difficulties in determining the optimal timing for ACP discussions, the lack of standardized procedures, and inadequate mechanisms for updating patients’ preferences. Against this backdrop, this paper reviewed the current application status of EHR in ACP, analyzed domestic and international research progress as well as existing problems, and explored feasible pathways for the in-depth integration of EHR and ACP. The purpose of this paper is to promote the electronic management of ACP, improve the quality of medical decision-making, facilitate the informatization development of palliative care, and ultimately establish a more humanized, patient-centered healthcare system, while raising societal awareness and attention towards end-of-life medical planning.

Low treatment adherence among patients with hypertension has resulted in generally low blood pressure control rates in China. Mobile health (mHealth) technology enables efficient information exchange between healthcare providers and patients through methods such as mobile applications, wearable devices, and telemedicine, which can significantly improve the management efficiency of hypertension patients. With the widespread popularity of smartphones, the conditions for applying mHealth technology in health management for chronic diseases have become mature. In recent years, an increasing number of studies on the application of mHealth technology in the health management of hypertension patients have been reported. This article systematically reviewed the current application status of mHealth technology in the health management of hypertension patients, meanwhile pointed out the existing problems in the current application of this technology, and prospects its future research directions, aims to provide references for general practitioners to apply mHealth technology in the practice of hypertension health management.

The maxillary bone is less dense than the mandible. The loss of maxillary anterior teeth often leads to alveolar ridge resorption, which in turn results in insufficient bone volume in the maxillary anterior tooth area. The loss of maxillary posterior teeth is usually accompanied by maxillary sinus pneumatization, causing insufficient vertical bone volume in the edentulous area. For patients with severe maxillary bone defect who are not eligible for traditional prosthetic methods or free-hand implant surgery, achieving high-quality occlusal reconstruction through implant prosthodontics has become an urgent clinical challenge for dentists in implant surgeries for insufficient maxillary bone volume. With the advancement of oral implant technology, implant surgery guided by dynamic navigation systems has emerged as a research hotspot in the field of implantology. This paper reviewed the key applications and advantages of dynamic navigation systems in oral implant surgeries for insufficient maxillary bone volume, aiming to provide a reference for the clinical application of this technology in implant dentistry.

With the continuous advancement of communication technologies, the rapid development of the Internet of Things is profoundly transforming the healthcare sector, particularly in terms of medical device interconnection, which demonstrates significant potential. The establishment of an Internet of Things for medical devices enhances the efficiency of information flow and integration within hospitals, facilitates real-time collection of patient health data and medical environment parameters, and provides scientific evidence for clinical practice through in-depth data analysis. This further optimizes diagnosis and treatment processes and improves the quality and efficiency of medical services. Internet of Things-based medical device interconnection promotes the refined management of medical equipment by enabling real-time monitoring, intelligent scheduling, and efficient utilization, thereby substantially reducing hospital operating costs. It also plays a pivotal role in the construction of smart hospitals. Therefore, this paper has expounded the value of the construction of the Internet of Things for medical equipment, analyzed and compared the application status of medical equipment interconnection modes based on the Internet of Things, and discussed the development direction of medical equipment interconnection under Internet of Things technology. It is intended to provide forward-looking thinking and strategic suggestions for the development of smart healthcare, and promote the wide application and in-depth integration of medical equipment Internet of Things technology in the medical industry.

Cervical cancer is a tumor with a well-defined etiology that is potentially curable, and its main treatment modalities include surgery, radiotherapy, and chemotherapy. How to reduce the radiation dose to the bladder and improve patients’ local control rate during external beam radiotherapy (EBRT) is an urgent issue to be addressed in the implementation of precision radiotherapy. Radiotherapy-related complications of cervical cancer are mainly dominated by radiation cystitis, radiation proctitis, and small intestinal injury. Changes in bladder status exert a significant impact on the dose delivered to organs at risk; therefore, whole-process bladder protection is of great importance. This article systematically reviewed the key strategies for bladder protection in EBRT for cervical cancer, and analyzed the topic around three core phases: radiotherapy localization, treatment planning design, and treatment plan delivery. It aims to provide references for the clinical development of bladder protection protocols in EBRT for cervical cancer, achieve a balance between precise target volume irradiation and bladder protection, and ultimately improve patients’ treatment prognosis.

Blood gas analysis is widely applied in medicine, with a development history of over 200 years. Currently, the commonly used clinical method for blood gas analysis is invasive blood gas detection technology. Although this technology has been well-developed, it still presents numerous issues in clinical practice, such as infection, bleeding, and discontinuous monitoring caused by invasiveness. To address the limitations of invasive techniques, non-invasive blood gas monitoring technology has emerged and continued to evolve. Traditional non-invasive technologies, such as pulse oximeters and capnogram, have limitations in accuracy. Among new technologies, gas chromatography-mass spectrometry, tunable diode laser absorption spectroscopy, and photoacoustic spectroscopy have been gradually applied. This paper elaborated on the development history and detection technologies of blood gas analysis, explored the development of non-invasive blood gas analysis technology, with a particular focus on the application of cavity ring-down spectroscopy based on optics in non-invasive blood gas analysis monitoring technology, and prospected the future development direction of non-invasive blood gas analysis technology, aiming to provide references for the research and development of related equipment.

As a crucial method for monitoring children’s growth and development and diagnosing endocrine diseases, bone age assessment is mainly performed by analyzing X-ray images of the hand and wrist. However, the application of traditional X-ray imaging technology is often affected by background interference, which reduces the accuracy of assessment results. In recent years, deep learning technology has been widely applied in the field of bone age image processing, demonstrating significant advantages due to its ability to automatically identify and extract complex features. This paper reviewed the research progress of deep learning in addressing background redundancy in bone age images, focusing on methods such as region-of-interest extraction, background segmentation technology, and attention mechanisms. These methods can effectively eliminate redundant background information and improve the accuracy and efficiency of bone age assessment. Furthermore, this paper discussed the limitations of existing technologies and future development directions, aiming to provide new research ideas and practical guidance for the field of bone age assessment.

Total knee arthoplasty (TKA) is an effective method to treat severe knee lesions, but postoperative rehabilitation treatment is very important. In recent years, the postoperative rehabilitation treatment of TKA has achieved remarkable results. This paper comprehensively expounded the technical methods, including the innovation and optimization of physical therapy, such as early rehabilitation training, muscle strength training, joint range of motion training, etc., and emphasized the important role of rehabilitation AIDS in patients’ self-care and rehabilitation effect. The research progress of rehabilitation treatment after TKA was reviewed to provide reference for clinical practice and improve the quality of rehabilitation and life of patients.

As a key medical device that provides oxygen-enriched or pure oxygen in a hyperbaric environment, large-scale medical oxygen chambers have been increasingly widely used in clinical medicine. They are mainly used for treating hypoxic diseases, promoting wound healing, and alleviating symptoms of various chronic diseases. Their performance and quality are directly related to treatment efficacy and patient safety. This paper reviewed the key performance indicators of large-scale medical oxygen chambers and their influencing factors, systematically analyzed domestic and international methods for evaluating the performance and quality of oxygen chambers, and focused on discussing the research progress and technical specifications in aspects such as chamber structure, sealing performance, oxygen concentration control, environmental stability, and safety testing. Additionally, combined with industry development trends, it analyzed the deficiencies in performance evaluation and prospected for future research directions and technical breakthroughs, including intelligent detection, standardization system construction, and full-life-cycle quality management, aiming to provide references for the research and development, production, and standardization of medical oxygen chambers.

The left atrium not only regulates left ventricular filling volume but also serves as a key biomarker for cardiovascular diseases. After the occurrence of left ventricular diastolic dysfunction, left atrial function undergoes compensatory changes before alterations in left atrial volume and left ventricular function. Left atrial strain can reflect changes in left atrial function throughout the cardiac cycle. Imaging is the only method for evaluating left atrial strain. Therefore, this article reviewed traditional and novel imaging techniques for assessing left atrial strain, including echocardiography, cardiac magnetic resonance, and cardiac computed tomography. It also introduced the application of radiomics in this field, and analyzed the reproducibility of these imaging techniques as well as the feasibility and limitations of their current clinical applications, aiming to provide appropriate imaging techniques for personalized assessment of left atrial strain in patients, offer reliable diagnostic and therapeutic evidence for clinicians, and facilitate the diagnosis, prognostic evaluation, and formulation of treatment plans for cardiovascular diseases.

The management of medical consumables is a key task in deepening the reform of the medical and health system, and the governance of high-value medical consumables in particular is crucial for standardizing the medical market and safeguarding patients’ rights and interests. Currently, research on the full-life-cycle management of medical consumables has expanded from single-institution logistics management to a multi-dimensional approach, yet significant gaps remain. Specifically, it is difficult to align the intentions of stakeholders, and the interest conflicts among the four parties-government, medical institutions, enterprises, and patients-in areas such as policy implementation, cost control, and quality assurance have not been systematically resolved. This leads to prominent issues including an unclear market operation mechanism and information asymmetry, which hinder the rational allocation of medical resources. Against this backdrop, this paper, from the perspective of stakeholders, identified the four core stakeholder groups, sorted out their research status and interest demands across all stages of the medical consumables’ full life cycle including R&D, production, circulation, usage, and supervision & evaluation, and analyzed the points of interest conflict. Finally, targeted optimization suggestions were proposed, aiming to provide a scientific basis for the formulation of relevant policies, thereby promoting the fair and stable operation of the medical market, reducing interest conflicts, protecting patients’ rights and interests, and advancing the healthy and sustainable development of the medical industry.

Medical auxiliary diagnosis has become a critical research direction in deep learning. By leveraging multi-layer neural networks, deep learning enables the realization of non-linear mapping between inputs and specific outputs through extracting deep features from image data, particularly in capturing lesion features and revealing details that are inaccessible to the naked eye. The applications of deep learning in the field of gastric cancer have covered multiple key aspects, providing multi-dimensional technical support for the precise diagnosis and treatment of gastric cancer. This review mainly focused on the applications of deep learning networks in the precise automatic classification of gastric cancer, the relationship between deep features and genes, and prognostic evaluation, with an emphasis on summarizing the basic ideas and improvements of the models. It also summarized the existing problems and challenges in this field and prospects future research directions, aiming to provide new perspectives and methods for computer-aided diagnosis and offer diverse entry points for the accurate diagnosis and personalized treatment of patients.

Telemedicine, leveraging its advantages of breaking time and space barriers and enabling bidirectional data feedback and sharing, has emerged as a novel medical model for medical communication between patients with diabetic foot ulcer (DFU) and healthcare providers. With the rapid advancement of wearable devices, big data, artificial intelligence, and other technologies, a new wave of telemedicine development has been triggered, which has demonstrated unique strengths in the monitoring and management of DFU. However, due to technical limitations, risks of information and data leakage, and inadequate supporting policies, there remains a gap between practical application and clinical promotion of telemedicine. This paper elaborated on the concept of telemedicine, summarized its application forms in the monitoring and management of patients with DFU, and explored the acceptance of telemedicine among these patients and their relevant stakeholders. It also analyzed the shortcomings and challenges in its promotion, and put forward prospects and considerations, aiming to provide references for clinical research on telemedicine in patients with DFU, thereby alleviating the enormous pressure on resource allocation and operation management of the healthcare system.

The applications of new-generation information technologies such as big data, artificial intelligence, the Internet of Things, block chain, and virtual reality in medical equipment management are continuously innovating, which is expected to promote the transformation of medical equipment quality control models and improve the quality management level of medical institutions. Based on the current status and challenges of medical equipment quality control, this paper summarized the applications of new-generation information technologies in key links of medical equipment quality control, including data collection, intelligent analysis, information traceability, and simulation optimization. It also analyzed relevant cases and prospects for future trends, aiming to facilitate the reengineering and reform of quality management processes in medical institutions and drive medical equipment quality management towards intellectualization, networking, and refinement. Medical institutions should strengthen collaboration, transform innovative achievements, establish a full-process quality and safety traceability mechanism and a co-governance system, and enhance their safety assurance capabilities.

As a key energy device in modern minimally invasive surgery, the radiofrequency-ultrasonic scalpel, featuring the simultaneous delivery of both radiofrequency and ultrasonic energies, boasts significant advantages such as minimal trauma and rapid recovery, thus being widely used in clinical practice. Compared with traditional instruments, it performs better in reducing operation time, intraoperative bleeding, and postoperative complications. Currently, this device has played an important role in multiple fields including general surgery, urology, gynecology, and neurosurgery. Although domestic research and development have achieved remarkable outcomes, with domestic substitution realized in some fields, issues such as reliance on imported core materials and limited expansion in the international market remain. This review mainly summarized the structural principles, application scenarios, and innovation directions of the radiofrequency-ultrasonic scalpel, analyzed its prospects, and put forward improvement suggestions, aiming to provide references for its subsequent development.

Currently, the incidence of breast cancer remains high. As a crucial procedure following mastectomy, breast reconstruction is of great significance to patients’ physical and mental health. However, traditional surgeries are affected by factors such as surgeons’ experience and individual differences, leading to problems like insufficient precision and uncontrollable risks. The application of AI has addressed complex challenges in breast reconstruction for breast cancer patients, including difficulties in meeting high surgical precision requirements, inadequate risk prediction and management, and deficiencies in remote rehabilitation guidance. This has thereby improved patients’ quality of life and satisfaction, reduced the burden on medical staff, and promoted the advancement of clinical medical technology. This article provided an overview of AI, summarized its current applications in breast reconstruction for breast cancer patients, and analyzed existing challenges and limitations, aiming to offer new insights and references for the future clinical application of AI in breast reconstruction.

Knee Osteoarthritis (KOA) is an irreversible degenerative disease. In the early stage, it mainly involves the cartilage, and the pathological changes of cartilage occur much earlier than its morphological changes, making conventional magnetic resonance imaging of limited value. Magnetic resonance (MR) quantitative analysis enables quantitative assessment of early articular cartilage damage based on changes in different components of the cartilage. This paper systematically reviewed the current application status of various MR quantitative imaging methods in the early diagnosis of KOA, and analyzed the advantages and limitations of different MR quantitative methods in terms of sensitivity, specificity, imaging time, and hardware requirements. It is pointed out that improvements should be made in shortening data acquisition time, improving image quality, and reducing radiofrequency energy accumulation. Additionally, the development direction of integrating this field with artificial intelligence was prospected, aiming to provide references for early intervention and efficacy monitoring of KOA.

Pneumonia is a lung infection caused by microorganisms such as bacteria and viruses. In recent years, applying deep learning techniques to classify pneumonia in chest X-ray images has become one of the important research directions in the field of medical image processing and analysis. Compared to traditional classification methods, deep learning techniques, especially convolutional neural networks (CNN), can better extract useful features from pneumonia images and suppress irrelevant information, detecting lung diseases such as pneumonia earlier and providing patients with earlier treatment, thereby improvingtreatment efficacy. A review and summary of CNN methods for classifying pneumonia in chest X-ray images was conducted. Firstly, two datasets widely used for pneumonia image classification and their evaluation criteria were introduced in detail. Then, the existing problems in manual classification of pneumonia images were pointed out. Subsequently, the research progress of CNN based on these two datasets was highlighted. Finally, the current challenges and future prospects for classifying pneumonia in chest X-ray images were discussed.

As the clinical treatment of chronic pain and spasmodic diseases has gained increasing attention, implantable intrathecal drug delivery systems (IDDS) have become one of the key approaches for managing such conditions. In China, IDDS have been gradually applied in clinical practice for treating various refractory pain and spastic disorders, but the systems remain undomesticated, with imported products carrying high prices. This paper introduced the general structure, performance, and therapeutic scope of IDDS, and summarized the research progress in both software and hardware aspects at home and abroad. By reviewing the current clinical application status of IDDS domestically and internationally, and comparing/analyzing market demands with the monopolistic pricing of imported products, this study clarified the value of further domestication research and discussed the significance of domestic development as well as the clinical application prospects in China.

Pre-operative anxiety is an emotional disorder before an operation, and its occurrence is related to surgical stress and pressure. Pre-operative stress-related anxiety increases anaesthetic doses during surgery and is even associated with a decrease in long-term quality of life and survival after surgery. Traditional preoperative education may suffer from problems such as monotony, formality, and abstract content. The emergence and development of virtual reality (VR) technology offer a new avenue for non-drug intervention in anxiety. Currently, a number of studies have been reported on VR-based intervention for surgical anxiety, particularly in children and for certain surgical procedures. This paper aimed to analyse and evaluate previous clinical studies on VR-based preoperative anxiety intervention, clarify its research direction, and provide references for clinical research in terms of expanding application scenarios, enriching intervention contents, and improving the evaluation system.

As a global public health priority, Parkinson’s disease (PD) patient care has garnered significant public attention. Current clinical management of PD patients faces bottlenecks such as traditional diagnosis relying on subjective experience, lack of personalized home rehabilitation programs, and limited monitoring methods for non-motor symptoms. Although mobile health applications (mHealth APPs) have demonstrated advantages in convenience and cost-effectiveness for chronic disease management, domestic applications are still confined to basic nursing interventions via WeChat, with notable gaps in areas such as auxiliary diagnosis, precise assessment, and integrated traditional Chinese and Western medicine interventions. This paper introduced the application of mHealth APPs in PD patient health management, covering four aspects: assisting disease diagnosis, health assessment, health monitoring, and home rehabilitation training. It proposed that mHealth APPs can facilitate early PD detection, improve patients’ quality of life, and enhance the quality of healthcare services, aiming to provide references for domestic research on mobile health management of PD patients.

Impacted mandibular third molars (IMM3) are common and frequently encountered diseases in oral and maxillofacial surgery, and imaging examinations play a crucial role in their diagnosis and treatment. In recent years, significant breakthroughs have been made in the application of artificial intelligence (AI) in stomatology. Specifically, AI based on deep learning (DL) can accurately identify and segment IMM3 and surrounding anatomical structures. This not only improves the efficiency of correct interpretation of oral images but also provides clinicians with a more intuitive and precise basis for diagnosis and treatment. Currently, DL-based AI technology has become a research hotspot in clinical stomatology. Therefore, this article reviewed the recent application progress, existing challenges, and future development directions of DL-based AI in the diagnosis and treatment of IMM3, aiming to provide intelligent theoretical support and practical guidance for IMM3 management.

The ultra-high-definition surgical microscope plays a critical role in modern surgical procedures. Compared to traditional microscopes, the ultra-high-definition surgical microscope provides surgeons with clearer and more detailed views due to its high-resolution imaging technology. This assists in accurately identifying tissue structures and enhances both surgical safety and success rates. This article focused on the technological advancements of ultra-high-definition surgical microscopes, including core technologies such as optical systems, lighting systems, visualization systems, and multi-modal imaging. It also analyzed their applications in various types of surgeries and explores the integration of intelligent assistive technologies such as augmented reality and artificial intelligence. Additionally, the article summarized the challenges faced by current technologies, such as the large size of equipment, lighting control issues, and data transmission limitations, aiming to provide reference for researchers and clinicians, promoting innovative applications of ultra-high-definition surgical microscopes in the medical field.

Positron emission tomography (PET) imaging is of great significance in clinical applications. The automated segmentation of its images is crucial for disease diagnosis and treatment. In recent years, remarkable progress has been made in the research of automated PET image segmentation, especially in the application of deep learning methods. PET image segmentation can accurately isolate and label regions of interest, such as tumors, organs, and other structures, which is of great importance for disease diagnosis, treatment planning, and research. This paper provided an overview of the latest research achievements in PET image segmentation based on neural network systems, analyzed the existing limitations and future development trends, aiming to facilitate the optimization of PET image segmentation algorithms and provide new ideas and references for medical research.

Nitric oxide (NO), as a signaling molecule with critical biological functions, is involved in various physiological processes such as vasodilation, neurotransmission, and immune regulation. As a key device for NO delivery, NO therapeutic devices have demonstrated unique advantages in the treatment of respiratory diseases, cardiovascular diseases, and tumors by precisely controlling gas concentration and release patterns. However, challenges in technical design and clinical application arise from NO’s extremely short half-life, strong concentration dependence, and potential toxicity. This paper reviewed the development history of NO therapeutic devices, covering research progress at home and abroad, technical structures, classification methods, and explored current dilemmas and future development trends, aiming to provide a reference for in-depth research in this field and facilitate broader applications of NO therapeutic devices in medicine.

With the increasing demand for hospital informatization management, problems such as low efficiency of manual entry, difficult traceability, and data silos in traditional medical consumables management have become more and more prominent. The unique device identification (UDI) of medical devices, as a core technology for achieving “one item, one code” whole-life cycle management, has become the key to breaking through the bottleneck of refined hospital management. This paper reviewed the relevant regulations and application status of UDI at home and abroad, as well as the applications of UDI in the refined qualification management, order management, inventory management, usage management, and traceability management of medical consumables in hospitals, aiming to provide references for the informatization and refined management of hospitals, as well as the whole-life cycle management and development of medical consumables.

The extracellular matrix (ECM), a pericellular network composed of various proteins, polysaccharides, and other components, is essential for maintaining the function of normal tissues and organs. Abnormal deposition and modification of ECM may lead to pathological conditions, such as the occurrence and progression of fibrosis and tumors. As a novel non-invasive parameter for quantifying ECM, the extracellular volume fraction (ECV) can reflect the microenvironment of tumors and evaluate the degree of tissue fibrosis. It holds significant clinical value for clinicians in assessing the severity of abdominal organ fibrosis, predicting the differentiation of abdominal tumor tissues, differentiating tumor types, and evaluating treatment efficacy. This article reviewed the measurement of CT-ECV and its applications in abdominal diseases, aiming to provide new technologies and perspectives for the diagnosis and treatment of abdominal diseases and improve the level of clinical management.

Traditional Chinese medicine rehabilitation is a new specialty that combines China’s national conditions and adapts to the needs of rehabilitation development. With the emergence of forward-looking technologies such as artificial intelligence, machine learning, and deep learning, the cross-integration of multiple disciplines and intelligent applications have also achieved good results in facilitating the development of traditional Chinese medicine rehabilitation. To promote the modernization of traditional Chinese medicine, fully integrate traditional Chinese medicine rehabilitation with artificial intelligence, and enable the field of traditional Chinese medicine to reach a new height based on modern science and technology, this paper discussed the combination of traditional Chinese medicine rehabilitation and artificial intelligence technology from the two perspectives of traditional Chinese medicine rehabilitation assessment and treatment, aiming to provide a frontier direction and theoretical support for the further development of intelligent traditional Chinese medicine rehabilitation.