PDF(467 KB)
Supervision System of AI-based Software as a Medical Device
Jiannan Zhang, Yingying Li, Jiahui Zhou, Yelin Zhu, Lanjuan Li
Strategic Study of CAE ›› 2022, Vol. 24 ›› Issue (1) : 198-204.
PDF(467 KB)
PDF(467 KB)
Supervision System of AI-based Software as a Medical Device
A scientific supervision system enhances vigorous and standardized development of emerging things. Artificial intelligencebased software as a medical device (AI-Based SaMD) is an important product in the health field enabled by artificial intelligence (AI). As AI develops further, its unique black box algorithm and independent learning ability have posed major supervision challenges. AIBased SaMD supervision needs to keep pace with the times and a more scientific supervision strategy is urgently required to minimize the adverse events of AI-Based SaMD and their risk impact. This article summarizes the current status of supervision systems and supporting resources of AI-Based SaMD in China and abroad considering the difficulties in algorithm change management, quality control, and safety traceability. In addition, we explore the problems and challenges of AI-Based SaMD in China. Furthermore, we suggest that the AI-Based SaMD supervision and support systems should be improved in China to overcome the disadvantages of postmarket supervision.
software as a medical device (SaMD) / artificial intelligence (AI) / supervision science
| [1] |
IMDRF SaMD Working Group. Software as a Medical Device (SaMD): Key definitions [C].Washington D.C.: IMDRF, 2013.
|
| [2] |
Matheny M E, Whicher D, Israni S T. Artificial intelligence in health care: A report from the National Academy of Medicine [J]. The Journal of the American Medical Association, 2020, 323:509– 510.
|
| [3] |
Benjamens S, Dhunnoo P，Meskó B. The state of artificial intelligence-based FDA-approved medical devices and algorithms: An online database [J]. npj Digital Medicine, 2020, 3(1): 118.
|
| [4] |
European Commission. ATI - Artificial Intelligence-based software as a medical device [R]. Brussels: European Commission, 2020.
|
| [5] |
FDA. Proposed regulatory framework for modifications to Artificial Intelligence/ Machine Learning (AI/ML)—Based Software as a Medical Device (SaMD) [R]. Silver Spring: FDA, 2019.
|
| [6] |
IMDRF. Software as a Medical Device: Possible framework for risk categorization and corresponding considerations [C]. Washington D.C.: IMDRF, 2014.
|
| [7] |
Abràmoff M D, Lavin P T, Birch M, et al. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices [J]. npj Digital Medicine, 2018, 1: 39.
|
| [8] |
严舒, 徐东紫, 欧阳昭连. 美国人工智能医疗器械监管与应用分 析 [J]. 中国医疗设备 2021, 36(2): 117–122. Yan S, Xu D Z, Ouyang Z L. Analysis on the regulation and Application of United States Intelligence medical device [J]. China Medical Devices, 2021, 36(2): 117–122.
|
| [9] |
FDA. How is the FDA considering regulation of Artificial Intelligence and machine learning medical devices?[R/OL]. (2021-09-21)[2021-11-10]. https://www.fda.gov/medical-devices/ software-medical-device-samd/artificial-intelligence-andmachine-learning-software-medical-device#regulation.
|
| [10] |
国家药品监督管理局. 人工智能医用软件产品分类界定指导原 则 [R]. 北京：国家药品监督管理局, 2021. National Medical Products Administration. Guiding principles for the classification and definition of Artificial Intelligence medical software products [R]. Beijing: National Medical Products Administration, 2021.
|
| [11] |
国家药品监督管理局. 监管科学研究稳步推进 重点项目攻关成 果丰硕 [N]. 中国医药报, 2021-04-15(002). National Medical Products Administration. Scientific regulatory research has steadily made fruitful results in key projects [N]. China Medical News, 2021-04-15(002).
|
| [12] |
赵飞, 高明献, 刘璞, 等. 136例国际医疗器械严重不良事件的统 计与分析 [J].中国医疗器械杂志, 2020, 44(2):166–171. Zhao F, Gao M X, Liu P, et al. Statistics and analysis of 136 cases of adverse events of international medical devices [J]. Chinese Journal of Medical Instrumentation, 2020, 44 (2): 166–171.
|
| [13] |
蔡小舒, 吕晖, 于广军. 美国FDA医疗人工智能软件审核指南研 究 [J].中国数字医学, 2019, 14(11): 34–37, 33. Cai X S, Lyu H,Yu G J. Study on audit guidelines of US FDA medical Artificial Intelligence software [J].China Digital Medicine, 2019, 14(11): 34–37, 33.
|
| [14] |
Ministry of Health, Labour and Welfare (MHLW). Update on medical device and IVD regulation in Japan [R/OL].(2021-03-20) [2021-11-10]. https://www.pmda.go.jp/files/000234056.pdf.
|
| [15] |
FDA. Artificial Intelligence/Machine Learning(AI/ML)-Based Software as a Medical Device(SaMD) action plan[R/OL]. (2021-01-15)[2021-11-10].https://www.fda.gov/media/145022/ download.
|
| [16] |
ITU.Whitepaper for the ITU/WHO focus group on Artificial Intelligence for health [EB/OL]. (2020-10-13)[2021-11-10]. https:// www.itu.int/go/fgai4h.
|
| [17] |
AI4EU.Verifiable and explainable risk forecasting Artificial Intelligence framework [EB/OL].（2021-11-26）[2021-11-28]. https://www.ai4europe.eu/business-and-industry/healthcare.
|
| [18] |
National Institutes for Food and Drug Control. The kick-off meeting of the “Development and application demonstration of the full life cycle testing platform for medical Artificial Intelligence products” was held online[EB/OL].（2020-08-03）[2021-11-10]. https:// www.nifdc. org.cn/nifdc/gzdt/ywdt/20200803095421924.html.
|
| [19] |
Wang Z F, Huang X L, Yang J，et al. Universal adversarial perturbation generated by attacking layer-wise relevance propagation [C]. New Yok: IEEE, 2020: 431–436.
|
| [20] |
王士泉，李言生，苏明亮，等. 医疗人工智能产品应用效果的评 估框架与流程研究 [J]. 医疗卫生装备, 2020, 41(1): 62–65. Wang S Q, Li Y S, Su M L, et al. Medical Artificial Intelligence product application evaluation framework and process research [J]. Chinese Medical Equipment Journal, 2020, 41(1): 62–65.
|
/
| 〈 |
|
〉 |
AI Summary
