Email Alert RSS
Volume 14 Issue 6
Nov.  2023
Turn off MathJax
Article Contents
Abstract
References
Related Articles
ZHENG Xinya, HUANG Yunyou, ZHANG Yiting, WENG Shengjie, ZHAN Jianfeng, ZHAGN Zhifei. Medical Artificial Intelligence Standard System: History and Current Status[J]. Medical Journal of Peking Union Medical College Hospital, 2023, 14(6): 1135-1141. DOI: 10.12290/xhyxzz.2023-0428
Citation: ZHENG Xinya, HUANG Yunyou, ZHANG Yiting, WENG Shengjie, ZHAN Jianfeng, ZHAGN Zhifei. Medical Artificial Intelligence Standard System: History and Current Status[J]. Medical Journal of Peking Union Medical College Hospital, 2023, 14(6): 1135-1141. DOI: 10.12290/xhyxzz.2023-0428
shu

Medical Artificial Intelligence Standard System: History and Current Status

  • 1.

    Capital Medical University School of Basic Medical Sciences, Beijing 100069, China

  • 2.

    School of Computer Science and Engineering, Guangxi Normal University, Guilin, Guangxi 541000, China

  • 3.

    Research Center for Advanced Computer Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100086, China

Funds: 

Strategic Priority Research Program of the Chinese Academy of Sciences XDA0320000

Strategic Priority Research Program of the Chinese Academy of Sciences XDA0320300

Guangxi Science and Technology Electric Power Data Analysis Research GuiKe AD20297004

More Information
  • Corresponding author:

    ZHAGN Zhifei, E-mail: zhifeiz@ccmu.edu.cn

  • Received Date: September 10, 2023
  • Accepted Date: October 29, 2023
  • Issue Publish Date: November 29, 2023
    Graphical Abstract
    • Abstract
  • The standardization of medical artificial intelligence (AI) is currently in its infancy and falls short of meeting the needs for the development, deployment, control, assessment, and guidance of medical AI products. This not only makes it difficult to standardize the research and development process and therefore increase the cost and affect the quality of the products, but also leads to challenges in achieving unified interaction, comparison, and evaluation of AI products. It may result in incorrect estimation and evaluation of products, thus misguiding the direction of medical AI development. Consequently, establishing a mature and unified standard system for medical AI has become an urgent priority. To facilitate the advancement of the medical AI standard system from its nascent stage to maturity, we conduct an in-depth analysis of the development history of medical AI standards from four aspects: medical data standards, standard datasets, benchmarks, and norms/guidelines. By revealing the problems in the current medical AI standards, we aim to provide a reference for related research.
    • FullText(HTML)
    • References (37)
  • [1]
    Shehab M, Abualigah L, Shambour Q, et al. Machine learning in medical applications: A review of state-of-the-art methods[J]. Comput Biol Med, 2022, 145: 105458. DOI: 10.1016/j.compbiomed.2022.105458
    [2]
    Rajpurkar P, Chen E, Banerjee O, et al. AI in health and medicine[J]. Nat Med, 2022, 28: 31-38. DOI: 10.1038/s41591-021-01614-0
    [3]
    Varma JR, Fernando S, Ting BY, et al. The Global Use of Artificial Intelligence in the Undergraduate Medical Curriculum: A Systematic Review[J]. Cureus, 2023, 15: e39701.
    [4]
    Grunhut1 J, Marques O, WyattNeeds ATM. Challenges, and Applications of Artificial Intelligence in Medical Education Curriculum[J]. JMIR Med Educ, 2022, 8: e35587. DOI: 10.2196/35587
    [5]
    Sim JZT, Fong QW, Huang W, et al. Machine learning in medicine: what clinicians should know[J]. Singapore Med J, 2023, 64: 91-97.
    [6]
    国家药品监督管理局. 人工智能医疗器械质量要求和评价第4部分: 可追溯性[EB/OL]. (2023-09-05)[2023-11-10]. https://std.samr.gov.cn/hb/search/stdHBDetailed?id=05E9A95426E17056E06397BE0A0A1931.
    [7]
    史雪莲, 陈敏. 医疗人工智能标准体系研究[J]. 中国卫生信息管理杂志, 2019, 12: 759-762. https://www.cnki.com.cn/Article/CJFDTOTAL-WSGL201906027.htm
    [8]
    张知非, 杨郑鑫, 黄运有, 等. 医学大数据与人工智能标准体系: 现状、机遇与挑战[J]. 协和医学杂志, 2021, 12: 614-620. DOI: 10.12290/xhyxzz.2021-0472
    [9]
    马琳, 邓宏勇. 国内外临床研究元数据标准: 特点及现状[J]. 中国循证医学杂志, 2023, 23: 478-484. https://www.cnki.com.cn/Article/CJFDTOTAL-ZZXZ202304009.htm
    [10]
    周晓梅, 李烁, 崇雨田, 等. 临床研究数据标准化工作的思考[J]. 临床内科杂志, 2022, 39: 790-792. https://www.cnki.com.cn/Article/CJFDTOTAL-LCLZ202211024.htm
    [11]
    Lee AJ, Kim KW, Shin Y, et al. CDISC-compliant clinical trial imaging management system with automatic verification and data Transformation: Focusing on tumor response assessment data in clinical trials[J]. J Biomed Inform, 2021, 117: 103782. DOI: 10.1016/j.jbi.2021.103782
    [12]
    Duda SN, Kennedy N, Conway D, et al. HL7 FHIR-based tools and initiatives to support clinical research: a scoping review[J]. J Am Med Inform Assoc, 2022, 29: 1642-1653. DOI: 10.1093/jamia/ocac105
    [13]
    Ayaz M, Pasha MF, Alzahrani MY, et al. The Fast Health Interoperability Resources (FHIR) Standard: Systematic Literature Review of Implementations, Applications, Challenges and Opportunities[J]. JMIR Med Inform, 2021, 9: e21929. DOI: 10.2196/21929
    [14]
    张丽鑫, 钱庆, 唐明坤, 等. 人工智能医疗器械标准数据集构建现状[J]. 中华医学图书情报杂志, 2021, 30: 1-8. https://www.cnki.com.cn/Article/CJFDTOTAL-YXTS202108001.htm
    [15]
    Wu B, Fu L, Guo X, et al. Multi-omics profiling and digital image analysis reveal the potential prognostic and immunotherapeutic properties of CD93 in stomach adenocarcinoma[J]. Front Immunol, 2023, 25: 984816.
    [16]
    石镇维, 刘再毅. 重视医学影像人工智能数据库的标准化建设[J]. 协和医学杂志, 2021, 12: 599-601. DOI: 10.12290/xhyxzz.2021-0507
    [17]
    马兆毅, 蒋薇, 宋超, 等. 肺部病变多模态影像和乳腺癌X线医学人工智能标准数据集研究[J]. 科技成果管理与研究, 2023, 18: 89-91.
    [18]
    王辉, 刘靖雅, 李哲, 等. 人工智能心电数据库的研究与应用[J]. 中国医疗设备, 2020, 35: 17-22. https://www.cnki.com.cn/Article/CJFDTOTAL-YLSX202010003.htm
    [19]
    Yoo H, Yum Y, Park SW, et al. Standardized Database of 12-Lead Electrocardiograms with a Common Standard for the Promotion of Cardiovascular Research: KURIAS-ECG[J]. Healthc Inform Res, 2023, 29: 132-144.
    [20]
    于伟泓, 张潇, 吴婵, 等. 糖尿病视网膜病变眼底彩照人工智能研究标准数据库的建立规范[J]. 协和医学杂志, 2021, 12: 684-688. DOI: 10.12290/xhyxzz.2021-0613
    [21]
    中华医学会外科学分会外科手术学学组, 中华医学会器官移植学分会肝移植学组, 中国医师协会器官移植医师分会移植免疫学专业委员会. 肝脏移植标准数据集[J]. 器官移植, 2020, 11: 126-175. https://www.cnki.com.cn/Article/CJFDTOTAL-QGYZ202001021.htm
    [22]
    Zhou N, Yuan X, Du Q, et al. FerrDb V2: update of the manually curated database of ferroptosis regulators and ferroptosis-disease associations[J]. Nucleic Acids Res, 2023, 51: D571-D582.
    [23]
    Kaushal A, Altman R, Langlotz, C. Geographic distribution of US cohorts used to train deep learning algorithms[J]. J Am Med Assoc, 2020, 324: 1212-1213.
    [24]
    WHO. WHO Issues First Global Report on Artificial Intelligence (AI) in Health and Six Guiding Principles for Its Design and Use[EB/OL]. (2021-08-28)[2023-11-10]. https://www.who.int/news/item/28-06-2021-who-issues-first-global-report-on-ai-in-health-and-six-guiding-principles-for-its-design-and-use.
    [25]
    Blagec K, Kraiger J, Frühwirt W, et al. Benchmark datasets driving artificial intelligence development fail to capture the needs of medical professionals[J]. J Biomed Inform, 2023, 137: 104274.
    [26]
    Gao Y, Dligach D, Miller T, et al. DR. BENCH: Diagno-stic Reasoning Benchmark for Clinical Natural Language Processing[J]. J Biomed Inform, 2023, 138: 104286.
    [27]
    Karargyris A, Umeton R, Sheller MJ, et al. Federated benchmarking of medical artificial intelligence with MedPerf[J]. Nat Mach Intell, 2023, 5: 799-810.
    [28]
    Werdiger F, Visser M, Bivard A, et al. Benchmark dataset for clot detection in ischemic stroke vessel-based imaging: CODEC-Ⅳ[J]. Neuroimage, 2023, 271: 119985.
    [29]
    Wagner M, Müller-Stich BP, Kisilenko A, et al. Compara-tive validation of machine learning algorithms for surgical workflow and skill analysis with the HeiChole benchmark[J]. Med Image Anal, 2023, 86: 102770.
    [30]
    Singhal K, Azizi S, Tu T, et al. Large language models encode clinical knowledge[J]. Nature, 2023, 620: 172-180.
    [31]
    Walker HL, Ghani S, Kuemmerli C, et al. Reliability of Medical Information Provided by ChatGPT: Assessment Against Clinical Guidelines and Patient Information Quality Instrument[J]. J Med Internet Res, 2023, 25: e47479.
    [32]
    Biever C. The easy intelligence tests that AI chatbots fail[J]. Nature, 2023, 619: 686-689.
    [33]
    李志勇, 邱晓岚, 杨建龙, 等. WHO《为基于人工智能的医疗设备生成证据: 训练、验证和评估框架》解析及其对我国医学装备行业的启示[J]. 中国医学装备, 2022, 19: 157-167. https://www.cnki.com.cn/Article/CJFDTOTAL-YXZB202207034.htm
    [34]
    Niemiec E. Will the EU Medical Device Regulation help to improve the safety and performance of medical AI devices?[J]. Digit Health, 2022, 30: 20552076221089079.
    [35]
    国家标准化管理委员会. 信息技术人工智能平台计算资源规范[EB/OL]. (2022-10-12)[2023-11-10]. https://std.samr.gov.cn/gb/search/gbDetailed?id=EB58F4DA9161B2A2E05397BE0A0A7D33.
    [36]
    国务院. 关于加强科技伦理治理的意见[EB/OL]. (2022-03-20)[2023-11-10]. https://www.gov.cn/zhengce/2022-03/20/content_5680105.htm.
    [37]
    国务院. 生成式人工智能服务管理暂行办法[EB/OL]. (2023-07-10)[2023-11-10]. https://www.gov.cn/zhengce/zhengceku/202307/content_6891752.htm.
    • Related Articles

  • Related Articles

    [1]GONG Liyun, WANG Xiaomei, PENG Guoqing, YU Huan, TAO Xiaoman. Reporting Guidelines for Healthcare Guideline Adaptations: An Interpretation of the RIGHT-Ad@pt Checklist[J]. Medical Journal of Peking Union Medical College Hospital, 2025, 16(1): 204-215. DOI: 10.12290/xhyxzz.2024-0063
    [2]ZHOU Qi, LI Qinyuan, HE Hongfeng, PENG Dongrui, ZHANG Huayu, WANG Ye, YANG Kehu, CHEN Yaolong. A Standardized Approach to Recommending Diagnostic Criteria in Chinese Clinical Practice Guidelines[J]. Medical Journal of Peking Union Medical College Hospital, 2024, 15(6): 1432-1438. DOI: 10.12290/xhyxzz.2024-0729
    [3]ZHANG Shan, LIU Jie. Interpretation of NCCN Clinical Practice Guidelines for Primary Cutaneous Lymphomas (Version 1.2024) Based on the Current Diagnosis and Treatment Status of China[J]. Medical Journal of Peking Union Medical College Hospital, 2024, 15(5): 1029-1037. DOI: 10.12290/xhyxzz.2024-0605
    [4]LIANG Yan. Interpretation on the 2023 AHA/ACC/ACCP/ASPC/NLA/PCNA Guidelines for the Management of Patients with Chronic Coronary Disease[J]. Medical Journal of Peking Union Medical College Hospital, 2024, 15(2): 312-319. DOI: 10.12290/xhyxzz.2024-0043
    [5]WANG Ling, REN Yaxuan, LUO Xufei, ZHU Di, LI Zhewei, WANG Ye, WANG Bingyi, ZHANG Huayu, YANG Shu, CHEN Yaolong. Analysis of the Current Status of China's Adaptation Guidelines[J]. Medical Journal of Peking Union Medical College Hospital, 2024, 15(1): 192-201. DOI: 10.12290/xhyxzz.2023-0545
    [6]GUAN Xinmiao, ZHU Yanzi, LIU Hao, LUO Minjing, LIANG Changhao, CAO Feng, LIU Zhihan, ZHOU Jianguo, ZHANG Dong, FEI Yutong. Requirements and Technical Aspects of Real-world Data Governance in China's Medical Standards and Guidelines[J]. Medical Journal of Peking Union Medical College Hospital. DOI: 10.12290/xhyxzz.2024-0409
    [7]ZHOU Qi, LI Qinyuan, LIU Yali, LUO Zhengxiu, ZHANG Weishe, CHEN Tong, LI Guobao, SHANG Hongcai, YANG Kehu, ZHANG Bo, CHEN Yaolong, ZHANG Shuyang. The Development of Guidelines for Rare Diseases: Past, Present and Future[J]. Medical Journal of Peking Union Medical College Hospital, 2023, 14(3): 621-628. DOI: 10.12290/xhyxzz.2022-0360
    [8]Zhejiang University, National Institutes for Food and Drug Control, Shanghai Changzheng Hospital. Expert Consensus on General Methods for Performance Evaluation of Artificial Intelligence Medical Devices (2023)[J]. Medical Journal of Peking Union Medical College Hospital, 2023, 14(3): 494-503. DOI: 10.12290/xhyxzz.2023-0137
    [9]LIU Yuan, ZHAO Lin. Update and Interpretation of 2022 National Comprehensive Cancer Network Clinical Practice Guidelines for Gastric Cancer[J]. Medical Journal of Peking Union Medical College Hospital, 2022, 13(6): 999-1004. DOI: 10.12290/xhyxzz.2022-0271
    [10]ZHANG Zhifei, YANG Zhengxin, HUANG Yunyou, ZHAN Jianfeng. Big Medical Data and Medical AI Standards: Status Quo, Opportunities and Challenges[J]. Medical Journal of Peking Union Medical College Hospital, 2021, 12(5): 614-620. DOI: 10.12290/xhyxzz.2021-0472

  • Cited by

    Periodical cited type(10)

    1. 魏雪瑶，郭敬鹏，李功靖. 基于集成学习的医疗数据治理与多源数据融合研究. 电子设计工程. 2025(03): 34-37+42 .
    2. 闫佳佳，寇楠楠. 人工智能应用于阿尔茨海默病影像诊断中的伦理审视. 中国医学伦理学. 2025(02): 179-186 .
    3. 胡佳敏，邱艳，任菁菁. AI在基层医疗慢性病管理中的应用研究进展. 中华全科医学. 2024(03): 481-485 .
    4. 刘美亚，孔庆锋，陈萍. 人工智能在胎儿超声心动图中的发展现况. 影像研究与医学应用. 2024(15): 7-9+15 .
    5. 阳雄宇，金献忠，陈建国，金菲英. 检验检测行业研究进展与发展建议. 中国标准化. 2024(15): 226-234 .
    6. 马冰峰，郭平，张威. 食品快速检测管理标准体系构建实践创新. 中国食品安全. 2024(01): 65-69 .
    7. 曹宏伟，施佳，权秦，马晓菁. 智慧检验系统在海军基层医疗机构中的应用与评价. 中华航海医学与高气压医学杂志. 2024(04): 524-528 .
    8. 徐芬芬，陈志强，李艳芳，张浩然，韩淑明，杨经纬，王广新. 儿科医生对人工智能的知信行现状调查与分析. 妇儿健康导刊. 2024(22): 164-168 .
    9. 杨诚，黎峥，曾锦，曹丹. 人工智能应用于眼科专业人才培养的挑战与对策. 科技管理研究. 2024(21): 131-138 .
    10. 田倩飞，陈云伟，黄小容，杨胜利. 数字医学研究发展现状与挑战. 世界科技研究与发展. 2024(06): 792-813 .

    Other cited types(0)

Catalog

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (703) PDF downloads (258) Cited by(10)
    Related
    Links: National Health Commission of the People's Republic of China Chinese Academy of Medical Sciences(CAMS) AMLC Check CNKI Database CNKI Term Online Chinese General Practice
    Address：Room 302,Third Floor,Building 7, No. 1 Shuaifuyuan, Wangfujing, Dongcheng District, Beijing 100730, China
    Tel：010-69154261/4262
    E - mail：medj@pumch.cn mjpumch@126.com
    Copyright of website：Editorial Office of Medical Journal of Peking Union Medical College Hospital

    Supported by: Beijing Renhe Information Technology Co., Ltd.  Baidu Analytics

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return
    x Close Forever Close