Objective With the rapid development of blockchain technology, its application in the medical field is receiving increasing attention. This article aims to study and analyze the application status, classification, development trends, and risk challenges of blockchain, providing guidance and reference for future research and practice. Methods This article explores, studies, and summarizes numerous cases of domestic medical institutions using blockchain technology through case analysis. Results Blockchain technology, with its significant potential and advantages, is widely used in scenarios such as data sharing, data storage, traceability management and identity authentication in the medical field. With the development of technology, blockchain will be applied in various fields such as medical research and clinical trials, medical IoT devices, and the combination of big data and AI technology in the future. Conclusion The application prospects of blockchain technology in the medical field are broad, but it still faces challenges such as security risks and social risks. Future research needs to focus on these challenges and explore how to further optimize the application of blockchain technology in the medical field.

As the core of medical artificial intelligence (AI) application, algorithms may cause discrimination, while improring the value of data, which is urgent need of legal regulation. This paper explains the connotation of medical AI algorithm discrimination and analyzes its generation mechanism, including the discrimination of health and medical data, the logic defect of algorithm and the doctor-patient interaction bias. Aiming at the shortcomings of the existing regulation theories, including algorithm accountability model, data protection model and comprehensive governance model, this paper proposes a path to optimize legal regulation, including formulating scenario-based legal regulation schemes, standardizing the use of health and medical big data, enhancing the explainability of algorithms, and integrating health data rights protection and technical regulation, so as to promote the fair use of medical AI algorithms.

Objective To assist the monitoring,treatment and research of common geriatric diseases through applying intelligent technology,so as to solve the geriatric medical problems brought about by the accelerated aging population.Methods With the help of medical Internet of Things(IoT)smart devices,the necessary out-of-hospital health data of the elderly was monitored and integrated in real time,and a closed-loop health data collection for common geriatric diseases was formed.An integrated cloud monitoring platform for common geriatric diseases and a multidimensional health data center platform was established.Based on big data governance technology and AI technology,the research of clinical intelligent recommendation decision-making model for geriatric diseases was carried out.Results The integrated cloud monitoring platform has been applied in some hospitals on a pilot basis,while the multi-dimensional health data center platform for common geriatric diseases has been initially built,effectively making up for the shortcomings of the previous geriatric health data center.Using more than 220,000 geriatric health data and hospital clinical knowledge base,a knowledge map of common geriatric diseases was constructed and a clinical intelligent recommendation decision-making rule engine was initially formed.Based on the knowledge map,the intelligent rule engine and intelligent recommendation decision-making model,clinical intelligent recommendation decision-making support for common geriatric diseases were realized with the help of the integrated application of CDSS.Conclusion This study has a positive impact on promoting regional geriatrics IT application,hierarchical diagnosis and treatment,clinical decision-making support and chronic disease management.

On the basis of literature analysis and expert consultation, the Delphi method and analytic hierarchy process were used to determine the indicators and weights at all levels, and a three-level evaluation index system for the informatization construction of the provincial CDC was constructed with organizational and management, facility safety, functional application and development effectiveness as the main framework, including 4 first-level indicators, 10 second -level indicators and 34 third-level indicators, among which the weight coefficients of the first -level indicators were 0.256, 0.252, 0.260 and 0.231,  respectively. In this study, an evaluation indicators system for the construction of  provincial CDC was preliminarily established, which laid the foundation for further verification and application.

This study aims to examine the development status and application prospects of urban pre-hospital first aid information system. Pre-hospital first aid is an important part of urban medical rescue operations, and enhancing the capacity of IT application is of great significance to improve the efficiency and quality of first-aid services. China has implemented a number of measures to improve the level of IT application in first aid services, and established a standardized pre-hospital network layout and IT application framework. This research proposes a construction plan of the urban pre-hospital first aid information system based on new technology applications, including 5G medical emergency private network, emergency operations center and accurate scheduling system. The proposed system intends to enhance the response speed and quality of emergency services by means of real-time data sharing, accurate positioning and scheduling, and remote medical consultations, thereby realizing the seamless integration of pre-hospital and in-hospital information, which enables the critical information from emergency scenes and patients' vital signs be transmitted to hospitals in real time, so as to provide scientific, precise decision-making support for medical professionals and dispatchers. By discussing the pre-hospital first aid information system, this study offers a scientific basis and technical  path for enhancing the quality and efficiency of pre-hospital fir Pre-hospital first aid services in urban areas of China.

Objective To enhance the informatization and intelligence of maternal and child health management.Methods Artificial intelligence, real-time data sharing and exchange, mobile health management and other technologies were used to establish a smart maternal and child health information management model, and a smart maternal and child health management information platform was built, which realizes vital signs collection, health monitoring, early warning intervention and follow-up management throughout pregnancy, childbirth and postpartum rehabilitation and other stages. Results Under the whole-process intelligent management model, the time required for maternal and child healthcare services was significantly reduced, the completeness and accuracy of medical records were significantly improved, maternal satisfaction and overall experience in healthcare services were also significantly enhanced (P＜0.05). Conclusion The smart maternal and child health information management platform has realized the informatization of the whole process of  maternal and child healthcare services, and improved the quality of medical services in hospital.

Objective To explore the construction and application of clinical research data platforms. Methods Taking the First Hospital affiliated to University of Science and Technology of China (Anhui Provincial Hospital) as an example, a technical framework of clinical research data platform was constructed, a four-tier data system of operational data storage, clinical data center, research data center, and specialized disease database was established. Data collection and integration of hospital information system were carried out, and data quality control, data labeling, and data security management were carried out to establish a high-quality data platform that can support clinical research. Results The clinical research data platform achieved independent and streamlined clinical research services such as medical data retrieval, specialized disease management and data analysis. Conclusion Clinical research data platform can deposit data assets, meet the data usage needs of medical research, so as to promote the transformation of data from passive support to active intelligence, and stimulate data productivity.

Objective To develop an intelligent medical record coding model integrating deep learning and knowledge graph technologies, so as to solve the problems such as low quality and efficiency of traditional manual medical record coding methods. Methods First of all, Natural Language Processing (NLP) technology was employed to develop an electronic medical record information extraction model, which can extract text feature vectors related to electronic medical record coding and identify clinical diagnosis entities. Secondly, a text multi-label classification model was designed to classify and code clinical diagnosis entities, and obtain preliminary ICD codes. Then, a core term matching model based on the knowledge graph was constructed to further select the preliminary coding results. Finally, two comparative experiments were designed and implemented to compare the coding performance of the intelligent medical record coding model with traditional methods and conventional manual coding. Results The coding performance of the intelligent medical record coding model developed in this study was better than that of the convolutional neutral network (CNN) model, the BiLSTM+Attention mechanism model and the intelligent medical record coding model without simple data disturbance enhancement presented herein. The accuracies of principal diagnosis coding and principal operation coding concerning malignant tumors from the intelligent medical record  coding model were 96.6% and 98.9% respectively, which were significantly higher than the accuracies of the traditional manual coding (85.3% and 89.4%). Conclusion The intelligent medical record coding model can enhance the quality of medical record coding while improving coding efficiency, which can provide higher quality data support for subsequent DRG/DIP pre-grouping and fine operational management.

Objective To improve the traditional performance appraisal methods, such as addressing the problems of unclear appraisal indicators and unstandardized data collection and analysis. Methods Using technologies such as AI and natural language processing (NLP), the scattered data of various systems were integrated and managed in a unified manner, stored in the data center in the form of reported indicators, so as to achieve the universal definition of indicators, build a performance appraisal system, display and analyze the trend of related indicators, and dynamically track and manage them. Results The platform integrated functions of display, statistics, analysis and feedback, achieved intelligent indicator analysis, fine operation management, dynamic performance monitoring and early warning, and improved the data quality. Conclusion The application of this system improves the accuracy and efficiency of hospital performance appraisal, and can provide decision-making support for hospital management.

Objective To use information technology to build a "one-stop" smart nursing work platform. Methods A knowledge base of various types of rules was built around clinical nursing work, and driven by the "reminder scheduling" engine, the decentralized nursing system was integrated to build a "one-stop" smart nursing work platform.Results The "one-stop" processing was realized in the clinical nursing platform, which supported the reminder and inquiry of nursing work, and enabling quality control and verification of the completion of nursing work. Conclusion The construction and application of the platform can integrated information technology into clinical nursing work more comprehensively, solve the problem of nurses' frequent operation among multiple nursing business systems, and significantly improve the work efficiency of nurses.

Objective Corresponding author: ZHANG Lei, Email: zhanglei@niha.org.cn To explore the data governance of general hospitals in the big data era, promote the rational utilization of information medical data in smart hospitals, and promote the high-quality development of smart hospitals. Methods By analyzing the difficulties of data governance in IT application development of general hospitals, a unified, clear and standardized data framework was established, a unified index system was formulated, and refined data quality management norms were implemented, which were verified by actual construction results. Results Through standards-based data governance, the management level of medical data and its resources was improved, the efficiency of clinical  research was enhanced, and the innovation of medical process and the optimization of medical model were promoted. Conclusion In the era of big data, the data governance of smart hospitals based on unified standards can be replicated and promoted, which can also help data elements realize the economic benefits and social value of the  multiplier effect of new productive force.

While bringing revolutionary changes to the medical industry, artificial intelligence (AI) also brought ethical, legal and social challenges, which has posed a severe test to AI governance frameworks around the world, and spawned an urgent need for new governance tools. With its unique adaptability and flexibility, soft law governance has become an important means to address the legal challenges faced medical by AI intelligence. Soft law has obvious advantages in responding to technological development, meeting social needs, and adapting to cultural diversity, but it also faces practical challenges such as insufficient refinement, difficulty in balancing interests, and poor implementation effects. In the future, China should further strengthen the advantages of soft law governance in medical AI, overcome its shortcomings. By enhancing the effective convergence between soft law and hard law, strengthening global cooperation in soft law, accelerating the localized and integration of soft law, and improving the transparency and public participation of soft law, the implementation effect of soft law can be improved, so as to promote the healthy development of medical AI.

In thousands of known rare diseases, approximately 30% to 40% of patients have facial abnormalities.Therefore, facial feature recognition has become an effective method for predicting rare disease risks and types by using machine learning technology. However, due to the scarcity of rare disease samples, machine learning faces many challenges. Dlib was used to extract 68 key facial points of the patients, and the dimensional distance between 35 points and 26 segment angle cosines were calculated as facial geometric features. The top 10 important features were selected through feature selection and integrated with 128-dimensional vectors provided by Dlib to construct 138-dimensional vectors as facial features. The facial data of DiGeorge  syndrome, Down syndrome and Williams syndrome published on the NIH website were used for classification modeling. The test results indicate that the model integrated with geometric features outperforms the model using only deep learning features in classification performance. This method not only offers better interpretability, but also is suitable for early screening and classification of rare diseases in small sample scenarios, providing an effective new method for predicting rare diseases.

By using the method of literature analysis, this paper systematically reviews the present situation of clinical application of AI medical devices abroad. The pre-market approval, listed products, post-market regulatory system and content of AI medical devices in the United States were introduced in detail. It is believed that the risk level setting in China was higher than that in the United States, the examination and approval is more stringent, but China's  regulatory system needs to be improved.

Objective In order to make better use of the real-world data of cervical cancer diagnosis and treatment,and improve the level of cervical cancer prevention, control, diagnosis and treatment. Methods Using the big data technology framework, based on the analysis of business, data and other process, the data of cervical cancer patients in the First Affiliated Hospital of Zhengzhou University was gathered, and a multimodal cervical cancer specialized database was constructed through data governance and system design of the database. Results Based on 12 clinical information systems, a multimodal cervical cancer specialized database covering 64 data models and 2,393 fields was constructed, including more than 22,000 patients and 309,000 visits. A special disease database management system covering 8 functional modules was developed, including data portrait, patient panorama, research project, data analysis, follow-up center, auxiliary diagnosis and treatment, knowledge base, and special disease database management. Conclusion Through this disease-specific database, the cross-system information of the same patient can be associated and integrated, and multi-modal, standardized, convenient and high-quality data can be obtained for clinical research of cervical cancer.

Objective To build an online doctor-patient relationship service platform based on "Internet+" technology, which can solve doctor-patient issues to achieve the whole process and fine management, provide convenient communication channels and quick response mechanism for patients, and be widely applied in hospitals within the medical consortium. Methods The hospital's WeChat service platform was used to integrate the hospital integration platform with the hospital information system to realize data sharing, so as to build a "patient oriented and service centered" analytical doctor-patient relationship service platform based on "Internet +". Results It has achieved four significant improvements in mechanism construction, work efficiency, evaluation,  accountability and supervision system construction and average satisfaction of the masses. Conclusion The doctor-patient relationship service platform has achieved good development results and has been widely used in the city's health system.

Objective To explore its current applications and prospects in pathology image processing, diagnostic support, and medical information management systems. Methods To review the evolution of intelligent pathology and its specific applications in pathology image processing and diagnostic support. We analyze the innovative role of deep learning technology in pathology image recognition and analysis. Using a real-world application case in a hospital as an example, we summarize the advantages and potential of intelligent pathology systems in improving diagnostic accuracy and workflow efficiency. Additionally, we conduct an analysis of the technical challenges that need to be addressed.Results Deep learning technology exhibits revolutionary performance in pathology image analysis, and intelligent pathology systems show significant potential in enhancing diagnostic accuracy and workflow efficiency. Conclusion The intelligent pathology department construction, leveraging intelligent pathology systems, demonstrates significant

advantages, but also presents technical challenges that need to be addressed.

As the front-end of collecting patients' personal information, medical institution Apps serve as a good starting point for medical institutions to carry out personal information protection. Based on the data assessment of 30 medical institution Apps from 7 aspects, including privacy policy compliance, unauthorized collection, unauthorized transmission and storage, unauthorized processing and use, unauthorized transmission and storage, unauthorized processing and use, illegal disclosure and provision, illegal deletion and other miscellaneous aspects, this paper analyzes the problems existing in the personal information protection of medical institution Apps and proposes suggestions for further standardization of personal information protection.

With the in-depth advancement of the construction of medical alliances, data sharing and business collaboration have become the core challenges in enhancing the utilization efficiency of medical resources and optimizing the service experience for patients.The traditional centralized data management model, due to its prominent security risks, lack of unified standards, and absence of mutual trust mechanisms, is difficult to meet the practical needs of the multi-subject collaborative development of medical alliances.This study proposes a construction plan for a medical consortium master data platform based on blockchain technology. By leveraging the decentralized, immutable, and secure encryption features of blockchain, combined with master data management technology, an intelligent platform has been constructed, which includes core modules such as a unified patient identity identification system, a medical staff practice information database, and a standardized data dictionary. The results show that this scheme performs excellently in terms of data integrity, consistency and security, and significantly improves access efficiency, providing a feasible technical path for the informatization construction of medical alliances.

Objective To enhance the service capacity and quality of independent clinical laboratories (ICL).Methods Based on traditional service processes, the entire workflow before, during, and after sample testing, as well as other personalized needs were reviewed. A unified customer service platform for ICL was constructed using a front-end and back-end separation model. Results The unified customer service platform includes features such as electronic report forms, smart report interpretation, electronic product manuals, and academic circles. It has received the Information Technology Security Level Protection Level 3 certification. The platform launched in April 2022, and as of March 2024, there were 84,368 physician users of the electronic report forms covering 13,359 hospitals, 22,812 physicians used the smart report interpretation, with the highest monthly view count reaching 25,461 times. Conclusion The ICL unified customer service platform meets users' needs for convenience and accuracy, promoting service standardization and unified management.