作者投稿
专家审稿
编辑办公
邮件订阅
RSS
Big Medical Data and Medical AI Standards: Status Quo, Opportunities and Challenges
-
摘要: 医学大数据和人工智能(artificial intelligence，AI)在提升医学资源利用率和服务质量方面具有极大的潜力，但同时也在隐私保护和技术风险方面带来挑战。标准是构造、评价和应用新技术的共识和规范，医学大数据和AI在临床的应用迫切需要制订数据、系统、计量标准以及应用和评价新技术的行为规范。本文定义了医学大数据与AI标准的内涵，包括数据相关标准、公共数据集、测试基准、行为规范；总结了医学大数据和AI标准的现状、潜在问题及挑战；在展望医学大数据与AI发展前景的同时，提出了结合大数据/AI增强的系统和医学科学大装置的系统新架构。Abstract: Big medical data and medical artificial intelligence (AI) not only have the great potential for improving the utilization of medical resources and the quality of medical service, but also pose challenges to privacy protection and technical risks. Standards are the consensus and norms for constructing, evaluating, and applying new technologies. The clinical application of big medical data and medical AI urgently needs regulations on data, systems, measurement standards, and codes of practice for evaluating new technologies. This paper defines big medical data and medical AI standards, including data-related standards, public datasets, benchmarks, codes of practice, and summarizes state-of-the-art and state-of-the-practice of big medical data and medical AI standards. While looking forward to the development prospect of big medical data and medical AI, we propose an innovative architecture consisting of big-data-and-AI-enhanced medical information systems and the big medical science infrastructure.
-
Key words:
- big medical data /
- medical artificial intelligence /
- standards /
- benchmarks /
- codes of practice
作者贡献：张知非负责撰写、修订论文；杨郑鑫和黄运有负责检索文献，撰写论文；詹剑锋提出选题思路，并撰写、审校论文。利益冲突：无 -
[1] Working Group 2 of the Joint Committee for Guides in Metrology. International vocabulary of metrology-Basic and general concepts and associated terms (VIM)[S]. [2021-06-08]. https://www.bipm.org/documents/20126/2071204/JCGM_200_2012.pdf. [2] McAllister J. Standards, Specifications, Protocols, Methods, and Codes[EB/OL]. (2020-09-16)[2021-06-08]. https://uark.libguides.com/Standards/Home. [3] International Organization for Standardization. Standardization and related activities-General vocabulary[S]. [2021-06-08]. https://www.iso.org/obp/ui/#iso:std:iso-iec:guide:2:ed-8:v1:en. [4] Bidgood WD Jr, Horii SC, Prior FW, et al. Understanding and using DICOM, the data interchange standard for biomedical imaging[J]. J Am Med Inform Assoc, 1997, 4: 199-212. doi: 10.1136/jamia.1997.0040199 [5] Wikipedia. Algorithm[EB/OL]. (2021-06-12)[2021-07-05]. https://en.wikipedia.org/wiki/Algorithm. [6] Price WN, Cohen IG. Privacy in the age of medical big data[J]. Nat Med, 2019, 25: 37-43. doi: 10.1038/s41591-018-0272-7 [7] World Health Organization. International Statistical Classification of Diseases and Related Health Problems 10th Revision[EB/OL]. [2021-06-08]. https://icd.who.int/browse10/2010/en. [8] LOINC. What LOINC is[EB/OL]. [2021-08-20]. https://loinc.org/get-started/what-loinc-is/. [9] SNOMED. SNOMED CT[EB/OL]. [2021-08-20]. https://www.snomed.org/snomed-ct/why-snomed-ct. [10] George WB, Stan H, Wesley R. HL7 V3 Message Development Framework[EB/OL]. (1999-11)[2021-08-20]. http://www.hl7.org/documentcenter/public/wg/mnm/Mdf99.pdf. [11] 国家卫生计生委. 关于发布《电子病历共享文档规范第1部分: 病历概要》等57项卫生行业标准的通告[EB/OL]. (2016-09-12)[2021-08-20]. http://www.nhc.gov.cn/fzs/s7852d/201609/37f11aacca5a49c2ad0984c8fc7a2873.shtml. [12] 中国医师协会. 关于发布《肝胆疾病标准数据规范》3项团体标准的公告[EB/OL]. (2020-10-10)[2021-08-20]. http://www.ttbz.org.cn/UploadFiles/StandardFpdFile/20201018233559493.pdf. [13] Rivera SC, Liu X, Chan AW, et al. Guidelines for clinical trial protocols for interventions involving artificial intelli-gence: the SPIRIT-AI extension[J]. Nat Med, 2020, 26: 1351-1363. doi: 10.1038/s41591-020-1037-7 [14] Rajpurkar P, Irvin J, Ball RL, et al. Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists[J]. PLoS Med, 2018, 15: e1002686. doi: 10.1371/journal.pmed.1002686 [15] De Fauw J, Ledsam JR, Romera-Paredes B, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease[J]. Nat Med, 2018, 24: 1342-1350. doi: 10.1038/s41591-018-0107-6 [16] Yim J, Chopra R, Spitz T, et al. Predicting conversion to wet age-related macular degeneration using deep learning[J]. Nat Med, 2020, 26: 892-899. doi: 10.1038/s41591-020-0867-7 [17] Kim H, Goo JM, Lee KH, et al. Preoperative CT-based deep learning model for predicting disease-free survival in patients with lung adenocarcinomas[J]. Radiology, 2020, 296: 216-224. doi: 10.1148/radiol.2020192764 [18] McKinney SM, Sieniek M, Godbole V, et al. International evaluation of an AI system for breast cancer screening[J]. Nature, 2020, 577: 89-94. doi: 10.1038/s41586-019-1799-6 [19] Wang P, Berzin TM, Glissen Brown JR, et al. Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study[J]. Gut, 2019, 68: 1813-1819. doi: 10.1136/gutjnl-2018-317500 [20] Tyler NS, Mosquera-Lopez CM, Wilson LM, et al. An artificial intelligence decision support system for the management of type 1 diabetes[J]. Nat Metab, 2020, 2: 612-619. doi: 10.1038/s42255-020-0212-y [21] 国家药品监督管理局医疗器械技术审评中心. 关于发布深度学习辅助决策医疗器械软件审评要点的通告[EB/OL]. (2019-07-03)[2021-07-05]. https://www.cmde.org.cn/CL0004/19360.html. [22] Food and Drug Administration. Artificial Intelligence and Machine Learning (AI/ML) Software as a Medical Device Action Plan[EB/OL]. (2021-01-12)[2021-07-05]. https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-soft-ware-medical-device. [23] Petersen RC, Aisen PS, Beckett LA, et al. Alzheimer's Disease Neuroimaging Initiative (ADNI): clinical characterization[J]. Neurology, 2010, 74: 201-209. doi: 10.1212/WNL.0b013e3181cb3e25 [24] Johnson A, Pollard T, Shen L, et al. MIMIC-Ⅲ, a freely accessible critical care database[J]. Sci Data, 2016, 3: 160035. doi: 10.1038/sdata.2016.35 [25] Tomczak K, Czerwinska P, Wiznerowicz M. The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge[J]. Contemp Oncol (Pozn), 2019, 19: A68-A77. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.809.8713&rep=rep1&type=pdf [26] Allen GI, Amoroso N, Anghel C, et al. Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease[J]. Alzheimers Dement, 2016, 12: 645-653. doi: 10.1016/j.jalz.2016.02.006 [27] Christov SC, Avrunin GS, Clarke LA, et al. A benchmark for evaluating software engineering techniques for improving medical processes[C]. Proceedings of the 2010 ICSE Workshop on Software Engineering in Health Care, 2010: 50-56. [28] Shamir L, Orlov N, Mark Eckley D, et al. ⅡCBU 2008: a proposed benchmark suite for biological image analysis[J]. Med Biol Eng Comput, 2008, 46: 943-947. doi: 10.1007/s11517-008-0380-5 [29] Zhang Z, Gao W, Zhang F, et al. Landscape of Big Medical Data: A Pragmatic Survey on Prioritized Tasks[J]. IEEE Access, 2019, 7: 15590-15611. doi: 10.1109/ACCESS.2019.2891948 [30] Liu X, Rivera SC, Moher D, et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension[J]. Nat Med, 2020, 26: 1364-1374. doi: 10.1038/s41591-020-1034-x [31] Act A. Health insurance portability and accountability act of 1996[J]. Public law, 1996, 104: 191. http://www.ehcca.com/presentations/hipaa6/foerster.pdf [32] 国家卫生健康委员会. 关于印发国家健康医疗大数据标准、安全和服务管理办法(试行)的通知[EB/OL]. (2018-09-14)[2021-06-08]. http://www.nhc.gov.cn/mohwsbwstjxxzx/s8553/201809/f346909ef17e41499ab766890a34bff7.shtml. [33] 全国人民代表大会. 中华人民共和国数据安全法[EB/OL]. (2021-06-10)[2021-07-02]. http://www.npc.gov.cn/npc/c30834/202106/7c9af12f51334a73b56d7938f99a788a.shtml. [34] 国务院. 中华人民共和国人类遗传资源管理条列[EB/OL]. (2019-06-10)[2021-07-02]. http://www.gov.cn/zhengce/content/2019-06/10/content_5398829.htm. [35] Liang Y, Guo Y, Gong Y, et al. FLBench: A Benchmark Suite for Federated Learning[C]. Intelligent Computing and Block Chain: First BenchCouncil International Federated Conferences, 2020: 166. [36] Rajkomar A, Oren E, Chen K, et al. Scalable and accurate deep learning with electronic health records[J]. NPJ Digit Med, 2018, 1: 1-10. doi: 10.1038/s41746-017-0008-y [37] Huang Y, Zhang Z, Wang N, et al. A new direction to promote the implementation of artificial intelligence in natural clinical settings[J]. arXiv preprint arXiv: 1905.02940. http://arxiv.org/abs/1905.02940 [38] Nagendran M, Chen Y, Lovejoy CA, et al. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies[J]. BMJ, 2020, 368: m689. http://www.bmj.com/cgi/content/abstract/368/mar23_22/m689 [39] Desai AN. Artificial Intelligence: Promise, Pitfalls, and Perspective[J]. JAMA, 2020, 323: 2448-2449. doi: 10.1001/jama.2020.8737 [40] Parikh RB, Obermeyer Z, Navathe AS. Regulation of predictive analytics in medicine[J]. Science, 2019, 363: 810-812. doi: 10.1126/science.aaw0029 [41] Chen H, Compton S, Hsiao O. DiabeticLink: A Health Big Data System for Patient Empowerment and Personalized Healthcare[C]. International Conference on Smart Health, 2013: 71-83. [42] Abràmoff MD, Lavin PT, Michele B, et al. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices[J]. NPJ Digit Med, 2018, 1: 1-8. doi: 10.1038/s41746-017-0008-y [43] Wang ZQ, Zhou YJ, Zhao YX, et al. Diagnostic accuracy of a deep learning approach to calculate FFR from coronary CT angiography[J]. J Geriatr Cardiol, 2019, 16: 42-48. http://med.wanfangdata.com.cn/Paper/Detail/PeriodicalPaper_lnxzbxzz-e201901006 -
下载:
点击查看大图
图(3)
计量
- 文章访问数: 1272
- HTML全文浏览量: 273
- PDF下载量: 347
- 被引次数: 0
