Carobene Anna, Cabitza Federico, Bernardini Sergio, Gopalan Raj, Lennerz Jochen K, Weir Clare, Cadamuro Janne
IRCCS San Raffaele Scientific Institute, Milan, Italy.
IRCCS Ospedale Galeazzi - Sant'Ambrogio, Milan, Italy.
Clin Chem Lab Med. 2022 Nov 3;61(4):535-543. doi: 10.1515/cclm-2022-1030. Print 2023 Mar 28.
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.
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.
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.
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.
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。
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。
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。
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。