• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

未来十年检验医学将何去何从?医学实验室高效整合与应用人工智能的合作模式。

Where is laboratory medicine headed in the next decade? Partnership model for efficient integration and adoption of artificial intelligence into medical laboratories.

作者信息

Carobene Anna, Cabitza Federico, Bernardini Sergio, Gopalan Raj, Lennerz Jochen K, Weir Clare, Cadamuro Janne

机构信息

IRCCS San Raffaele Scientific Institute, Milan, Italy.

IRCCS Ospedale Galeazzi - Sant'Ambrogio, Milan, Italy.

出版信息

Clin Chem Lab Med. 2022 Nov 3;61(4):535-543. doi: 10.1515/cclm-2022-1030. Print 2023 Mar 28.

DOI:10.1515/cclm-2022-1030
PMID:36327445
Abstract

OBJECTIVES

The field of artificial intelligence (AI) has grown in the past 10 years. Despite the crucial role of laboratory diagnostics in clinical decision-making, we found that the majority of AI studies focus on surgery, radiology, and oncology, and there is little attention given to AI integration into laboratory medicine.

METHODS

We dedicated a session at the 3rd annual European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) strategic conference in 2022 to the topic of AI in the laboratory of the future. The speakers collaborated on generating a concise summary of the content that is presented in this paper.

RESULTS

The five key messages are (1) Laboratory specialists and technicians will continue to improve the analytical portfolio, diagnostic quality and laboratory turnaround times; (2) The modularized nature of laboratory processes is amenable to AI solutions; (3) Laboratory sub-specialization continues and from test selection to interpretation, tasks increase in complexity; (4) Expertise in AI implementation and partnerships with industry will emerge as a professional competency and require novel educational strategies for broad implementation; and (5) regulatory frameworks and guidances have to be adopted to new computational paradigms.

CONCLUSIONS

In summary, the speakers opine that the ability to convert the value-proposition of AI in the laboratory will rely heavily on hands-on expertise and well designed quality improvement initiative from within laboratory for improved patient care.

摘要

目标

在过去十年中,人工智能(AI)领域不断发展。尽管实验室诊断在临床决策中起着关键作用,但我们发现大多数人工智能研究都集中在外科、放射学和肿瘤学领域,很少有人关注将人工智能整合到检验医学中。

方法

在2022年第三届欧洲临床化学与检验医学联合会(EFLM)年度战略会议上,我们专门设置了一个关于未来实验室中的人工智能主题的环节。演讲者们共同合作,生成了本文所呈现内容的简要总结。

结果

五个关键信息是:(1)实验室专家和技术人员将继续改进分析项目、诊断质量和实验室周转时间;(2)实验室流程的模块化性质适合人工智能解决方案;(3)实验室亚专业不断发展,从检测选择到结果解读,任务的复杂性不断增加;(4)人工智能实施方面的专业知识以及与行业的合作将成为一种专业能力,需要新颖的教育策略来广泛实施;(5)监管框架和指南必须适应新的计算模式。

结论

总之,演讲者们认为,在实验室中转化人工智能价值主张的能力将在很大程度上依赖于实验室内部的实践专业知识和精心设计的质量改进举措,以改善患者护理。

相似文献

1
Where is laboratory medicine headed in the next decade? Partnership model for efficient integration and adoption of artificial intelligence into medical laboratories.未来十年检验医学将何去何从?医学实验室高效整合与应用人工智能的合作模式。
Clin Chem Lab Med. 2022 Nov 3;61(4):535-543. doi: 10.1515/cclm-2022-1030. Print 2023 Mar 28.
2
Potentials and pitfalls of ChatGPT and natural-language artificial intelligence models for the understanding of laboratory medicine test results. An assessment by the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Working Group on Artificial Intelligence (WG-AI).ChatGPT 和自然语言人工智能模型在理解检验医学结果方面的潜力和陷阱。欧洲临床化学和检验医学联合会(EFLM)人工智能工作组(WG-AI)的评估。
Clin Chem Lab Med. 2023 Apr 24;61(7):1158-1166. doi: 10.1515/cclm-2023-0355. Print 2023 Jun 27.
3
The Value of Artificial Intelligence in Laboratory Medicine.人工智能在检验医学中的价值
Am J Clin Pathol. 2021 May 18;155(6):823-831. doi: 10.1093/ajcp/aqaa170.
4
Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology.加拿大放射学家协会关于放射学人工智能的白皮书。
Can Assoc Radiol J. 2018 May;69(2):120-135. doi: 10.1016/j.carj.2018.02.002. Epub 2018 Apr 11.
5
Diagnostic quality model (DQM): an integrated framework for the assessment of diagnostic quality when using AI/ML.诊断质量模型(DQM):一种用于在使用人工智能/机器学习时评估诊断质量的综合框架。
Clin Chem Lab Med. 2023 Jan 25;61(4):544-557. doi: 10.1515/cclm-2022-1151. Print 2023 Mar 28.
6
Insights from semi-structured interviews on integrating artificial intelligence in clinical chemistry laboratory practices.从半结构化访谈中洞察人工智能在临床化学实验室实践中的整合。
BMC Med Educ. 2024 Feb 22;24(1):170. doi: 10.1186/s12909-024-05078-x.
7
Artificial Intelligence and Mapping a New Direction in Laboratory Medicine: A Review.人工智能与实验室医学的新方向:综述。
Clin Chem. 2021 Nov 1;67(11):1466-1482. doi: 10.1093/clinchem/hvab165.
8
Artificial Intelligence: Guidance for clinical imaging and therapeutic radiography professionals, a summary by the Society of Radiographers AI working group.人工智能:放射技师协会人工智能工作组的临床影像和治疗放射学专业人员指南摘要。
Radiography (Lond). 2021 Nov;27(4):1192-1202. doi: 10.1016/j.radi.2021.07.028. Epub 2021 Aug 20.
9
How do providers of artificial intelligence (AI) solutions propose and legitimize the values of their solutions for supporting diagnostic radiology workflow? A technography study in 2021.人工智能(AI)解决方案提供商如何为其支持诊断放射学工作流程的解决方案的价值观提出并使其合理化?一项 2021 年的技术志研究。
Eur Radiol. 2023 Feb;33(2):915-924. doi: 10.1007/s00330-022-09090-x. Epub 2022 Aug 18.
10
Rise of the Machines: Artificial Intelligence and the Clinical Laboratory.机器崛起:人工智能与临床实验室
J Appl Lab Med. 2021 Nov 1;6(6):1640-1654. doi: 10.1093/jalm/jfab075.

引用本文的文献

1
Medical students' knowledge, attitudes, and practices toward generative artificial intelligence in Egypt 2024: a Cross-Sectional study.2024年埃及医学生对生成式人工智能的知识、态度和实践:一项横断面研究。
BMC Med Educ. 2025 May 28;25(1):790. doi: 10.1186/s12909-025-07329-x.
2
Transforming Healthcare in the Age of Artificial Intelligence: A New Era of Diagnostic Excellence in Laboratory Medicine.人工智能时代的医疗变革:检验医学诊断卓越的新时代。
Indian J Clin Biochem. 2025 Apr;40(2):163-164. doi: 10.1007/s12291-025-01315-2. Epub 2025 Mar 5.
3
Artificial Intelligence Readiness Among Jordanian Medical Students: Using Medical Artificial Intelligence Readiness Scale For Medical Students (MAIRS-MS).
约旦医学生的人工智能准备情况:使用医学生医学人工智能准备量表(MAIRS-MS)
J Med Educ Curric Dev. 2024 Sep 19;11:23821205241281648. doi: 10.1177/23821205241281648. eCollection 2024 Jan-Dec.
4
Artificial intelligence in the pre-analytical phase: State-of-the art and future perspectives.分析前阶段的人工智能:现状与未来展望。
J Med Biochem. 2024 Jan 25;43(1):1-10. doi: 10.5937/jomb0-45936.
5
Insights from semi-structured interviews on integrating artificial intelligence in clinical chemistry laboratory practices.从半结构化访谈中洞察人工智能在临床化学实验室实践中的整合。
BMC Med Educ. 2024 Feb 22;24(1):170. doi: 10.1186/s12909-024-05078-x.
6
Interventions to improve appropriateness of laboratory testing in the intensive care unit: a narrative review.改善重症监护病房实验室检测适宜性的干预措施:一项叙述性综述。
Ann Intensive Care. 2024 Jan 15;14(1):9. doi: 10.1186/s13613-024-01244-y.
7
Exploring knowledge, attitudes, and practices towards artificial intelligence among health professions' students in Jordan.探索约旦卫生专业学生对人工智能的知识、态度和实践。
BMC Med Inform Decis Mak. 2023 Dec 14;23(1):288. doi: 10.1186/s12911-023-02403-0.