This study investigates the research progress and application practices of AI technologies in the field of cybersecurity. It traces the early exploration paths of AI technologies such as neural networks and deep learning in cybersecurity. It also expounds on AI-driven novel cybersecurity applications from four dimensions: penetration testing, code auditing, threat intelligence, and large models for vertical security domains. Subsequently, it conducts an in-depth analysis of the challenges faced by AI in cybersecurity, illustrating the security issues of AI in sensitive industries with a case study of the medical industry. It forecasts the development trends of the deep integration of AI and cybersecurity, providing references for promoting cyberspace governance capabilities.

To address the current issues of inaccuracies in asset identification technology for network access assets and low efficiency in asset information filling, this paper proposes an intelligent verification system for network access assets by leveraging existing hospital systems, including the Network Access Control System, Terminal Management System, and Equipment Registration System. First, the Network Access Control System conducts real-time screening of baseline data for all network assets. Then, from the backend of the Terminal Management System, information such as the operating system and security status of devices already installed with the software is extracted. Finally, this data is compared with asset records in the Original Equipment Registration System. Devices with identical IP addresses but discrepancies in other attributes are flagged as modified, and their information is verified after manual on-site confirmation. Tests demonstrate that the system achieves an asset accuracy rate of over 98.5%, and shortens asset localization time by 95%. The system significantly enhances the accuracy of hospital network asset data, accelerates online asset identification, and improves the efficiency of device localization within the network. This establishes a robust foundation for refined asset management and security practices in healthcare field.

The article grounded in the zero trust security model, proposes and implements a multi-layered defense architecture tailored to the security requirements of hospital research data platforms. This architecture ensures secure access control through a bastion host, employs sandboxing technology to guarantee data access isolation, and relies on distributed firewalls and a dynamic privilege management mechanism to safeguard data during remote logins by domain users. Additionally, the adoption of centralized domain control management strategies and internet behavior auditing effectively reduces the risk of sensitive data leakage. The research demonstrates that the proposed solution significantly enhances the security protection capabilities and compliance management level of hospital research data platforms, providing practical reference for the digital transformation of the healthcare industry.

Explore the development ideas and practical methods for the construction of a hospital cyber security operation system, and provide references for further enhancing the cyber security capabilities of hospitals. Through the analysis and summary of security operations team building, security operations platform construction, and security operations process development, this study explores approaches to establishing a cybersecurity operations system for hospitals. The hospital's cyber security monitoring, early warning, and response capabilities have been effectively enhanced, significantly improving the efficiency of handling security incidents. The construction of a cyber security operation system can further enhance a hospital's capabilities in monitoring, early warning, and responding to cyber security incidents, effectively ensuring the secure operation of its business.

With the rapid development of artificial intelligence technology, the application of AIGC (AI-Generated Content) in the healthcare field has become increasingly widespread, significantly enhancing the efficiency and quality of medical services. However, the deeper integration of AIGC technology also introduces security issues such as data misuse and data leakage. These threats not only endanger patient privacy and safety but could also disrupt the normal operation of the entire healthcare system. Based on the construction of the Suzhou Healthcare AI Competence Center, this study proposes a framework for securing the input and output content of large language models. Through the establishment of an AI-based monitoring and analysis system built upon large models, real-time monitoring of AIGC input/output data, detection of anomalous behaviors, and risk warnings are enabled, allowing for timely countermeasures before data misuse or leakage occurs. This ensures the security and compliance of medical data, effectively prevents the misuse of healthcare data, and safeguards patient data privacy. This research provides valuable insights and reference for secure AIGC application practices in the healthcare field.

Based on practical applications, this study aims to establish a trusted exchange system for medical and health data in the full-process traceability scenario of hospital laboratory samples using blockchain and its derivative technologies. The goal is to explore and advance the application of blockchain technology innovations in the healthcare sector. Blockchain and IPFS technologies were utilized to innovate and integrate the execution process and results of laboratory samples with a distributed architecture. This approach overcomes the traditional centralized or weakly centralized models of laboratory sample transfer, thereby establishing a trusted exchange system for medical and health data. By constructing a blockchain network and IPFS for laboratory samples, and leveraging features such as smart contracts and the non-repudiation nature of distributed ledgers, the distributed storage of internal and external unstructured medical documents on the blockchain and the entire process documentation of laboratory samples on the blockchain were achieved. The decentralized characteristics of blockchain distributed ledgers and IPFS were employed to ensure full traceability of the entire laboratory sample transfer process, with data being immutable. This further promotes the trusted sharing of medical data.

With the rapid development of medical technology and modern technology in China, there is an increasing demand for emergency medical systems to quickly reach the scene and provide effective treatment in the face of sudden and uncertain pre-hospital emergencies. In recent years, UAVs technology has been widely adopted in civilian fields, and its characteristics of high efficiency, multifunctionality, and low cost have demonstrated great potential in emergency medical rescue. By integrating with cutting-edge technologies such as artificial intelligence and 5G, the types and functions of UAVs are continuously enriched and upgraded, which may greatly enhance the efficiency of emergency medical services and optimize rescue models. Based on this, this paper reviews the current status of digital applications of UAVs in emergency medical systems. By deeply analyzing the current challenges and future development trends, this study aims to provide insights for the deep integration of UAVs and emerging digital technologies in emergency medical systems, so as to improve the rescue efficiency of medical personnel and the level of emergency medical treatment.

Realize the intelligent early warning of security events, and solve the problems of low inspection efficiency, insufficient video data application and analysis, passive event detection and lagging disposal in the traditional security work of hospitals. Based on the AI visual analysis center as the foundation of the entire system, fully utilizing technologies such as full target structured analysis of video scenes, AI parsing of video, AR panoramic technology, intelligent algorithm warehouse, etc., to meet the three major functional areas of key personnel prevention and control, intelligent security control, and refined comprehensive management of hospital security. The system has achieved precise targeting of targets, foreseeing the occurrence of security incidents, overall control of security situations, and scientific acquisition of security data in security management. The system can improve the efficiency of hospital safety management, assist in the refinement and intelligent upgrading of hospital management, and promote the high-quality development of hospitals.

To solve the problems such as poor follow-up effect and poor patient experience of day surgery patients, build an efficient whole-process data management platform for day surgery follow-up system, and provide effective data support for clinical decision-making and scientific research. The intelligent follow-up mode of automatic voice combined with artificial voice was adopted to integrate hospital follow-up resources, standardize follow-up content and follow-up time, and apply a variety of data visualization analysis methods to help the hospital have an in-depth understanding of the situation of day surgery. The successful telephone connection rate is comparable to that of conventional manual follow-up, which significantly reduce the labor cost and improved the efficiency of follow-up. The system provides a convenient and efficient follow-up communication channel for doctors and patients, which is of great significance in ensuring the quality and safety of patient recovery, improving the efficiency of hospital management, and reducing doctor-patient disputes.

China is one of the global centers of the diabetes epidemic. Effective prevention, treatment, and management of type 2 diabetes (T2D) are of great public health significance. Digital therapeutics (DTx), based on evidence-based medicine, utilize digital programs for disease intervention and have been widely applied in the management and treatment of T2D. This study reviews the domestic and international progress of DTx in the field of T2D, including their design and implementation, clinical effectiveness evaluation, and health economics evaluation. It also discusses existing issues and future trends, providing a reference for the development and promotion of DTx for T2D in China.

Exploring effective methods for diagnosing neonatal acute bilirubin encephalopathy,  from an imaging perspective, this study aims to develop computer-aided diagnostic approaches for neonatal acute bilirubin encephalopathy based on multimodal magnetic resonance imaging (MRI) data and deep learning techniques. Given the characteristics of limited sample size and small inter-class differences in neonatal acute bilirubin encephalopathy imaging, a shallow convolutional neural network (CNN) model suitable for diagnosing this condition is proposed. Additionally, a method for synthesizing training data through multimodal magnetic resonance imaging (MRI) fusion is employed. The diagnostic performance of the model is evaluated on different MRI modalities and their fused images. The shallow convolutional neural network model achieved an accuracy of 91.25% and an AUC value of 0.9129 on the multimodal MRI fusion dataset (including T1-weighted MRI, T2-weighted MRI, and apparent diffusion coefficient maps). These results outperformed the model's diagnostic performance on any single MRI modality and two-modal fusion images. The computer-aided diagnosis of neonatal acute bilirubin encephalopathy based on multimodal magnetic resonance imaging and deep learning technology proposed in this article has potential diagnostic advantages on NABE and can be further applied to actual clinical practice. 

Exploring the entity annotation standards of ancient medical cases, taking the Xin'an Medical Case as a reference, to offer insights for the development of a unified structured annotation protocol for ancient Chinese medical case records. Using 7 621 Xin'an Medical Cases as the subject of study, a collaborative approach involving independent annotation by multiple annotators and a combination of machine tagging with human review was employed for text preprocessing. This process involved identifying named entities and establishing entity annotation standards specific to the medical case context. The annotation of medical cases was completed through a multi-round iterative process of machine tagging and human review, with continuous refinement of the annotation standards to ensure accuracy and consistency. 19 naming entities and 17 entity relationships of ancient medical cases were identified, and the entity labeling specifications were clarified in detail. The results of the three pre-labeled consistency tests were 0.71, 0.78, and 0.82, respectively. Significant improvements have been noted in the consistency between automated entity annotations and human verification, as evidenced by enhanced precision (P), recall (R), and F-measure (F) scores. Owing to the structural similarities among ancient medical case texts, the named entity recognition and annotation protocols developed in this research are suitable for the structural annotation of general ancient medical case texts. This contributes a replicable approach and strategic insights to the informatization efforts in the field of ancient medical case documentation.

To explore the establishment of data management system for graded hospital evaluation through big data technology, to support the data management work of graded hospital evaluation, and to promote the high-quality development of hospitals. The data management system for graded hospital evaluation relying on big data technology was constructed. Through the integration of data from hospital business systems and the logical configuration of indicators, the system can accomplish automatic data capture, indicator statistics, data benchmark analysis, visual display, and data sharing, achieving systematic data management. To provide data for graded hospital evaluation and subsequent regular management, the system achieved automatic acquisition and intra-hospital sharing of indicators and guaranteed the standardization, accuracy, timeliness, and stability of data, and improves the efficiency of data management. This system rationalized the hospital data management system and enriched the connotation of hospital data governance, and promoted the establishing of a long-term mechanism for hospital data monitoring, and is of great significance for improving the scientific and systematic hospital management.

Effectively solve the "five management pain point" of traditional hospital contract drafting, approval and signing, query and borrowing, contract execution supervision, and business data sharing. Research relevant literature, collect contracts signed by a certain hospital in the past 2 years, analyze the key points of hospital contract control and policy requirements, design a digital contract management system suitable for the actual situation of our institute. Embed key internal control requirements such as bidding procurement, contract signing, and contract execution into the information system, interconnection and interoperability with other systems such as CA system, budget management system, reimbursement system, SMS platform, and dingTalk, real time perception of performance anomalies and risks, achieving full process supervision of  contracts. Established a full lifecycle contract management system that includes contract drafting, approval stamping, performance archiving, risk warning, and data analysis, 100% online approval of hospital contracts, automatic generation of electronic ledgers, and risk warning. This system helps regulate the economic activities of public hospitals, strengthen internal control and risk management, improve contract management efficiency and hospital fine management level, and assist in the high-quality development of public hospitals.

Explore the policy factors that may influence the implementation and participation outcomes of large-scale digital health intervention programs for chronic diseases, and provide optimization strategies for the implementation of large-scale digital health intervention programs for chronic diseases in China. Based on the research topic, 32 relevant studies were retrieved and analyzed, covering 12 large-scale digital health intervention projects for three chronic diseases. The study used Smith policy implementation process model to derive the relationships between various factors and put forward policy recommendations. The policy implementation entity, target group, and policy implementation environment interact with each other during the policy implementation process, and the content of policy design has an impact on the above-mentioned links at a higher level. There is still room for improvement in the optimization of policies related to digital health technologies. It is recommended that future policy design should focus on personalized design, primary prevention strategies, the integration and utilization of existing medical resources, infrastructure construction and information security construction in the context of new technologies, and conduct more health needs research in various scenarios from a comprehensive perspective.

In order to meet the requirements of DRG payment reform, this article designs a DRG disease group quality control-settlement-cost system framework to achieve information-based quality control and form a closed-loop management process throughout the entire process. Explore the DRG evaluation system and cost accounting system, design the system from four modules: inpatient information filling, medical record quality control, medical insurance settlement and analysis and disease group cost analysis, which will achieve the visualization of DRG indicator data and financial indicator data. The DRGs quality control system for process management has improved the enrollment rate of medical records and reduced the number of abnormal enrollment cases. By iterating and integrating existing information systems accelerate data flow speed and reduce manual errors because of the data interconnection and interoperability. Information systems have improved the standardization and accuracy of medical record data, providing efficient workflows for medical insurance quality control work, personalized data support for standardizing clinical diagnosis and treatment behavior, and policy data foundation for medical insurance process management and financial cost management.

Based on the module functions and data content associated with "Medical Insurance High-speed Railway" and National Negotiated Medicine , to explore the function mechanism and operation effect of "Medical Insurance High-speed Railway" platform from the three levels of medical insurance, medical services and pharmaceuticals, so as to provide feasible suggestions for the implementation and improvement of relevant policies. To summarize the implementation policies and main characteristics of Nanjing National Negotiated Medicine, and analyze the functions and effectiveness of the platform based on the module functions of the "Medical Insurance High-speed Railway" and the data of National Negotiated Medicine. The operation of the "Medical Insurance High-speed Railway" platform has realized the full-cycle dynamic monitoring of the relevant data of  National Negotiated Medicine in Nanjing, and the overall landing efficiency of National Negotiated Medicine showed a steady upward trend from 2019 to 2022. Nanjing "Medical Insurance High-speed Railway" gives full play to its strong data monitoring and analysis capabilities, helps the smooth landing and widespread use of National Negotiated Medicine, and promotes data sharing and collaborative governance among all parties.

This paper is to address the issues of non-standard management, uneven quality, and inconsistent standards of medical software, explore and analyze the key points of quality management under the background of medical informatization. This paper took Daping Hospital, Army Military Medical University as the research object, focusing on a series of core issues such as the process quality, product quality, usage quality, and operation and maintenance quality of the medical software in this hospital. It analyzes the elements of quality management and puts forward certain suggestions. The paper investigated and summarized the quality management of medical software in the cycle stage, listed the objective factors affecting the quality of medical software and summarized the key points of medical software quality management. The research results provide a certain reference value for the quality management of medical software, and help to strengthen the development of hospital medical information further.