15 December 2025, Volume 20 Issue 12

    

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  Research progress and trends in digital therapeutics applications

  LIU Yuhan, CHENG Yaping, YU Siwei, LU Long

  China Digital Medicine. 2025, 20(12): 1-8.

  Abstract ( )   Knowledge map   Save

  This study systematically reviews the fundamental concepts and core values of digital therapeutics, and

  comprehensively analyzes its current development landscape both globally and within China, focusing on regulatory

  frameworks, product typologies, and application domains. Furthermore, it identifies the major challenges that constrain

  DTx development, including insufficient clinical validation, limited patient adherence, incomplete reimbursement

  mechanisms, and data security vulnerabilities. Based on existing literature, the paper proposes strategic countermeasures

  to address these barriers and discusses future development trends in technological integration, policy support, and

  market expansion. The review aims to offer theoretical foundations and practical references for the clinical application

  and health policy formulation of digital therapeutics.
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  Analysis of digital therapeutics products characteristics in the US, Germany, and Belgium with implications for China

  XIANG Peng, FANG Qichuan, LEI Jianbo

  China Digital Medicine. 2025, 20(12): 9-18.

  Abstract ( )   Knowledge map   Save

  Objective To analyze the characteristics of approved Digital Therapeutics (DTx) products in the US,

  Germany, and Belgium, providing references for the development of China's DTx industry. Methods Data were

  sourced from the US FDA, German DiGA directory, and Belgian mHealthBELGIUM platform. Devices meeting the

  DTx definition were identified based on inclusion and exclusion criteria, and their characteristics were analyzed using

  descriptive statistics. Results By the end of 2024, a total of 272 DTx devices were included (US: 192; Germany: 55;

  Belgium: 25). Following a period of fluctuating growth, the approval rates in all three countries showed a slowdown

  in the past year. US products primarily focused on chronic disease management, largely leveraging mobile health 

  technologies. German products were concentrated on treating mental and behavioral disorders, predominantly

  using cognitive behavioral therapy. Belgian products emphasized health status monitoring in oncology and chronic

  diseases. Conclusion China should draw on these international experiences to improve its regulatory and payment

  systems, strengthen real-world evidence and clinical value verification, and promote the standardized and sustainable

  development of the DTx industry.
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  Clinical applications, challenges, and prospects of digital therapeutics in insomnia

  IANG Jiahui, YAO Pan, HUANG Lei.

  China Digital Medicine. 2025, 20(12): 19-26.

  Abstract ( )   Knowledge map   Save

  Insomnia, as the most common sleep disorder, faces challenges in traditional psychological treatment

  due to resource constraints, high costs, and limited accessibility, making it difficult to meet clinical demands. Digital

  therapeutics for insomnia (DTI) has consequently emerged, leveraging technologies such as mobile applications and

  online platforms to transform diverse application formats or theoretical treatment methods into flexible and accessible

  digital intervention solutions. DTI has a significant effect on improving insomnia and is cost-effective. However,

  the field still faces a series of challenges, including limited applicability across specific populations, suboptimal

  patient adherence, insufficient data security and regulatory frameworks, a lack of long-term efficacy evidence, and a

  homogeneity in intervention models. Future efforts should prioritize the creation of diversified intervention pathways,

  establish stepped-care models, and strengthen policy support and standardization efforts. These measures are essential to

  achieving sustainable clinical utility and commercial value for DTI.
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  The application and prospects of digital therapies in clinical nutritional intervention of foods for special medical purpose

  LIU Li, WANG Yingyu, ZHU Mengting, LI Dan, SUN Jing, YAN Jiai, YANG Ju, ZHANG Feng, CAO Hong

  China Digital Medicine. 2025, 20(12): 27-34.

  Abstract ( )   Knowledge map   Save

  This article systematically collates the integrated application practices of DTx and FSMP in typical

  chronic disease areas, including diabetes, post-chemoradiotherapy rehabilitation of tumors, chronic kidney disease,

  and sarcopenia. It further conducts an in-depth analysis of core challenges in their synergistic development, such

  as regulatory system adaptation, clinical validation standards, and data security governance, while outlining future

  technological iterations and development trajectories. The in-depth integration of DTx and FSMP is capable of

  transforming clinical nutrition from a standardized supply model to a dynamic targeted intervention paradigm. With

  the maturation of relevant technologies and the improvement of supporting mechanisms, this integration is expected

  to become a “standard configuration” for nutritional intervention in chronic diseases, providing crucial support for the

  construction of a data-driven precision nutrition system.
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  International experience and insights on the regulation of digital therapeutics applications

  WU Lantao, ZHENG Qiuying, QIU Yingpeng, TIAN Xueqing, REN Ping, SHI Shenghui, XIAO Yue, YOU Mao.

  China Digital Medicine. 2025, 20(12): 35-43.

  Abstract ( )   Knowledge map   Save

  Objective To summarize the experiences of Germany, the United Kingdom, the United States, and

  Japan in regulating the application of digital therapeutics, providing reference for improving relevant management

  systems in China. Methods Information from the official websites of regulatory authorities in the four countries was

  retrieved to sort out their policy frameworks and practical models. Results Germany ensures the quality of digital

  therapeutics products through a dedicated directory management mechanism; the UK integrates health technology

  assessment comprehensively into the regulatory process to ensure service safety and quality; the US implements a

  Software Precertification Pilot Program, focusing on evaluating manufacturers' capabilities and product safety; Japan

  implements the DASH strategy, adopting risk-based classification and dual regulation to strengthen product safety

  and efficacy. Conclusion China can draw on international experience to establish flexible registration, approval,

  and directory management mechanisms, improve the hierarchical classification system, construct a full-cycle health

  technology assessment framework, enhance full-process supervision before and after market launch, standardize

  prescription rights certification and management, establish a scientific pricing mechanism, and explore diversified 

  payment methods, thereby promoting the standardized and healthy development of digital therapeutics and better

  serving patients' health needs.
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  Construction and application of the refined management mode of medical consumables in whole-life cycle based on UDI

  WANG Tingting, LIANG Weixia, HUANG Beili

  China Digital Medicine. 2025, 20(12): 44-48、109.

  Abstract ( )   Knowledge map   Save

  Objective To construct the refined management system of the whole-life cycle for medical consumables

  based on unique device identification (UDI), and to address the challenges of extensive management of consumables.

  Methods By upgrading the consumables information management system, the UDI code management module was

  integrated in the supply chain platform, HIS, HRP, surgical anesthesia and SPD systems to promote multi-system

  information interconnection. Qualitative and quantitative research methods were used to compare the management

  efficiency indicators before and after UDI implementation. Results The application of UDI optimized the management

  efficiency of medical consumables in acceptance, inventory, use, charge, settlement, traceability and other aspects, and

  the acceptance warehousing time and billing time of consumables were substantially reduced (P＜0.001). Conclusion

  The implementation of UDI system is helpful to realize the closed-loop tracking of the whole process data of medical

  consumables, and provides key support for the refined transformation of consumables management.
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  Ideas and practice for managing medical record connotation quality based on large language models

  HOU Jie, LIU Ting, DUAN Congzhe.

  China Digital Medicine. 2025, 20(12): 49-54.

  Abstract ( )   Knowledge map   Save

  Objective To explore the application of large language models (LLMs) in the connotative quality

  management of medical records and analyze the facilitative effect of emerging technologies on medical quality

  improvement. Methods Using the "Quality Control Indicators for Medical Record Management (2021 Edition)"as

  the target indicators to be evaluated in this study. LLMs were deeply integrated with the clinical decision support

  system (CDSS), and structured desensitized medical data was utilized to train the model's proficiency in medical

  record text comprehension. Through functions such as real-time in-process reminders and natural language interaction,

  physicians were assisted in promptly detecting and rectifying connotative quality defects during the documentation

  process. The clinical application accuracy of LLMs was verified by comparing the connotative quality control defects

  identified by the model with manual adjudication results. Results All eight established connotative quality control

  rules achieved an accuracy rate exceeding 90% during the experiment, and three target indicators were improved, which

  was consistent with the experimental hypotheses. Conclusion LLMs exhibit superior capabilities over traditional

  artificial intelligence (AI) quality control systems in medical record quality management. They contribute to enhancing

  the connotative quality of medical records, strengthening physicians' awareness of documentation standardization,

  significantly alleviating the workload of clinical staff, quality control personnel, and medical record administrators,

  promoting the further optimization of the hospital's medical record quality management system, and aligning with the

  overall goal of high-quality hospital development.
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  Construction and practice of a multi-campus medical imaging data integration and intelligent diagnosis platform

  QUE Jianren, LING Sikai.

  China Digital Medicine. 2025, 20(12): 55-61.

  Abstract ( )   Knowledge map   Save

  Objective It effectively integrated and utilized medical imaging data across various hospital campuses,

  established a unified management standard for such data, so as to build a secure and efficient medical imaging data

  integration and intelligent diagnosis platform. Methods Based on a privatized platform architecture, distributed storage

  technology was adopted to achieve unified management of petabyte-level imaging data across multiple campuses. The

  DICOM standard and FHIR protocol were utilized for standardized and fusion of multimodal data (CT, MRI, ultrasound,

  endoscopy, nuclear medicine, etc.). Blockchain technology was implemented to ensure data security and privacy

  protection. Deep learning-based intelligent diagnosis modules (3D ResNet and Transformer models) were deployed to

  provide lesion detection, segmentation, and assisted diagnosis functions. Results The platform successfully integrated

  imaging data from hospitals within the Ruijin Medical Consortium, enabling second-level retrieval of cross-campus

  imaging data. The intelligent diagnosis module achieved over 96% accuracy in detecting typical conditions such as

  pulmonary nodules, stroke, and fractures, with performance exceeding traditional methods by over 15%. Conclusion

  The platform enables efficient fusion and intelligent application of multi-campus medical imaging data, significantly 

  enhancing clinical diagnostic efficiency and scientific research capabilities. It offers a scalable solution for regional

  medical resource sharing and smart hospital construction.
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  Research progress of artificial intelligence in difficult airway assessment

  SONG Changwei, CHEN Yining, ZHAO Qing, LI Jianqiang, GE Yanhu, BAI Yunbo, BAI Jing, WANG Sheng, YANG Jijiang.

  China Digital Medicine. 2025, 20(12): 61-68.

  Abstract ( )   Knowledge map   Save

  In the field of anesthesiology, the occurrence of difficult airways significantly increases the likelihood of 

  various complications, posing substantial challenges to patient safety. Preoperative assessment of difficult airways helps

  mitigate potential risks associated with them. However, current assessments heavily rely on the evaluator's personal

  experience and professional skill level, leading to subjective variability and potential inconsistency in results. The

  application of artificial intelligence (AI) in difficult airway assessment offers the ability to process and analyze vast

  amounts of data, reducing subjectivity, minimizing inter-evaluator discrepancies, lowering the risk of human error, and

  providing decision support. This review aims to comprehensively examine the application and research progress of

  AI technologies in the evaluation of difficult airways within the anesthesiology field over the past five years, covering

  the most advanced technologies and relevant literature. Additionally, this paper discusses the challenges faced by AI

   applications in this domain and explores future development directions. 
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  Innovative application of digital diagnosis in metabolic syndrome based on fusion of infrared thermography and radiomics

  GUO Jiayang, AN Yanhong, ZHANG Wenzheng, LI Xiaoran, CHEN Yu, XUE Huizhong, YANG Yimeng, XIAO Yonghua.

  China Digital Medicine. 2025, 20(12): 69-74、115.

  Abstract ( )   Knowledge map   Save

  Objective Develop a radiomics-based auxiliary diagnostic model for metabolic syndrome (MS)

  using hand thermal infrared imaging combined with machine learning techniques, enabling early screening and

  clinical decision support for MS. Methods Retrospective thermal infrared imaging data of 160 male subjects were

  collected between July 2017 and April 2023 and divided into a training set and a validation set in a 7:3 ratio. Features

  significantly associated with MS were identified using Pearson correlation analysis and LASSO regression, followed by

  the construction of a random forest prediction model. The SHAP (SHapley Additive exPlanations) method was applied

  to interpret the model and quantify the contribution of each feature to the prediction outcomes. Results Among the

  160 male subjects, 80 were healthy controls and 80 were MS patients. The constructed radiomics model achieved an 

  AUC of 0.88 in the training set and 0.85 in the validation set, demonstrating robust diagnostic performance. The SHAP

  analysis revealed the relevance of the original images, as well as the clustering tendency and contrast characteristics of

  the LBP feature maps within the model. Conclusion The radiomics model based on palm thermal infrared imaging

  provides a novel non-invasive approach for early MS screening. It exhibits excellent clinical application potential and

  can effectively assist physicians in early intervention and clinical decision-making.
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  Construction of an objective auxiliary evaluation system for non-specific low back pain posture data based on the holistic concept of Traditional Chinese Medicine

  YAN Wei, JIANG Yongmei, XU Cheng, LIU Kuan, ZHANG Jiahui, MA Honghong, ZHAO Yichen, CHENG Shunda, FANG Min, XI Xiaobing.

  China Digital Medicine. 2025, 20(12): 75-81.

  Abstract ( )   Knowledge map   Save

  Objective To address the issue of subjective assessment of non-specific low back pain in clinical

  practice, a non-specific low back pain efficacy auxiliary evaluation system based on dynamic functional analysis

  under the guidance of the holistic concept of traditional Chinese medicine is proposed, promoting the development

  of diagnosis and treatment of non-specific low back pain towards a more systematic and objective direction, and

  improving the evaluation level of non-specific low back pain. Methods Collect dynamic functional data of patients

  through motion capture and other technologies, develop intelligent motion hardware systems and supporting intelligent

  diagnosis and treatment software, and achieve real-time and accurate collection and synchronous analysis of dynamic

  functional data of non-specific low back pain patients. Research on data mining algorithms for mechanical information

  characteristics of non-specific low back pain patients, revealing their matching patterns with functional models of non

  specific low back pain patients, and achieving precise and objective evaluation of patients with this disease. To ensure

  the accuracy and systematicity of the evaluation system, clinical validation will be conducted to analyze the evaluation

  effect through feedback of biomechanical information and clinical comparison. Results This study designed an

  objective auxiliary evaluation system for non-specific low back pain posture data, and found through clinical validation

  that the A-GCN+MS-TCN model was significantly better than the traditional ST-GCN model in walking and motor

  function testing of non-specific low back pain patient skeleton datasets, with significantly improved recognition

  accuracy. Conclusion The objective auxiliary evaluation system for non-specific low back pain posture data based on

  the holistic concept of traditional Chinese medicine has achieved precise collection of limb function data information,

  which can improve the accuracy of clinical evaluation.
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  Analysis of data reporting quality on the national clinical improvement system in 2020－2022

  LIU Dong, PENG Qian, YANG Yang

  China Digital Medicine. 2025, 20(12): 82-87.

  Abstract ( )   Knowledge map   Save

  Objective To understand the quality of data reporting in the national clinical improvement system of

  medical institutions, guide medical institutions to continuously improve the quality of data reporting, promote the high

  quality development of hospitals, and provide a reference for relevant departments to formulate policies. Methods

  Based on the data released by the National Clinical Improvement system from 2020 to 2022, we analyzed the

  completeness of medical institutions, the overall “/” rate, the total star rating score, and differences in the type, level, and

  ownership of medical institutions in the full sample from 2022. Results From 2020 to 2022, there were statistically

  significant differences in the completeness, overall “/” rate, and total star rating of medical institutions (P ＜ 0.001).

  In 2022, there were statistically significant differences in the type, level, and ownership between medical institutions

  that had submitted data and those that had not submitted (P＜0.01). Conclusion Medical institutions should further

  improve data filling and data quality control, and further reduce the overall “/” rate and improve the total star rating

  while ensuring integrity. Improving the submission rate of medical institutions and strengthening the management of

  data quality in specialized, level 2 and private medical institutions are the key to improving data quality.
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  Research and exploration on enhancing hospital network security operations capability based on large model technology

  SUN Guoqiang, LI Xiaoze, YANG Wei, HU Chenjie.

  China Digital Medicine. 2025, 20(12): 87-92.

  Abstract ( )   Knowledge map   Save

  Objective This study addresses issues such as difficulty in vulnerability repair, high data leakage

  risks, and frequent attacks in cybersecurity operations within the healthcare sector. We aim to create an intelligent

  security operation system based on large model technology to automate threat assessment and enhance cybersecurity 

  defense effectiveness. Methods We integrate dynamic rule generation with multimodal data fusion technology to

  design a layered collaborative architecture known as "Situation Awareness － Large Model", which is then validated

  in a real hospital environment. Results The security large models exhibited significant improvements compared to

  traditional cybersecurity systems in core healthcare scenarios, with alert noise reduction rising from 56.7% to 92.6%,

  and threat response times reduced to seconds, along with a substantial decrease in phishing email false positive rates.

  Conclusion Intelligent security operation system based on large model technology provided reusable industry

  solutions for the healthcare industry, promoting deep integration of AI and cybersecurity, and offering a reference path

  for addressing cybersecurity challenges in the healthcare sector.
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  Construction and practice of cybersecurity and boundary protection system in multi-campus hospitals

  MA Liming, HUANG Shaobin, JIANG Zhuobin, HUANG Guoxing, ZHANG Xinghua.

  China Digital Medicine. 2025, 20(12): 93-98.

  Abstract ( )   Knowledge map   Save

  Objective This paper provides a practical reference for constructing a network security system for

  medical institutions facing challenges in multi-campus and diverse information systems environments. Methods

  The system adheres to the “Information Security Technology－Baseline for Classified Protection of Cybersecurity”

  standard, employing a refined partitioned and zoned network architecture, combines high performance with highly

  available critical path design, integrates multi-level mature security protection technology and normalized network

  security operation, and constructs a unified security assurance system for multiple hospital areas. Results The

  system's effectiveness has been validated through multiple attack-defense drills, which promotes internal management

  and technical upgrading. Conclusion The successful establishment of an integrated architecture for network and

  information security across multi-campus hospitals enables centralized management of security incidents, enhances

  business continuity and security, and provides reference for the construction of cybersecurity systems in healthcare

  institutions.
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  Research on data security protection of China's disease prevention and control information system

  DU Xuejie, LI Shaoqiong, JIN Lizhu, ZHENG Huan, GUO Qing

  China Digital Medicine. 2025, 20(12): 99-102.

  Abstract ( )   Knowledge map   Save

  Objective To address the increasingly serious data security issues faced by China's disease prevention

  and control information system. Methods Analyze the current situation of data security in the system, explore data

  security protection measures according to the relevant requirements of national data security. Results Establish

  and implement a variety of data security mechanisms to effectively prevent data loss and leakage in the system.

  Conclusion Effective protection of system data security is a prerequisite for high-quality development of disease

  control information and a reference basis for public health data security management.
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  Construction and application of pre-hospital emergency resource management platform based on global quality control management

  ZHANG Zhelai, TANG Jingyun, ZHU Jie

  China Digital Medicine. 2025, 20(12): 103-109.

  Abstract ( )   Knowledge map   Save

  
  
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  Design and implementation of syndromic surveillance system based on large language model

  ZHANG Zhelai, TANG Jingyun, ZHU Jie.

  China Digital Medicine. 2025, 20(12): 110-115.

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  Objective Symptom surveillance is of great significance for the early warning of infectious diseases

  and public health emergencies. The existing symptom surveillance systems still rely on rule-based natural language

  processing technology, which is difficult to obtain high precision symptom recognition rate and affects the reliability

  and final effect of symptom surveillance. Methods A symptom monitoring system was designed and constructed

  based on the Suzhou Universal Health Information Platform. This system applied the large language model technology

  to the task of symptom entity recognition, specifically to identify symptoms in outpatient and emergency electronic

  medical records by fine-tuned BERT pre-training model. Results The introduction of large language model technology

  greatly improved the recall rate and accuracy rate of symptom recognition. Conclusion This system realized the high

  precision symptom monitoring of T+1 day in outpatient and emergency departments of public medical institutions in the

  city