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

被引:25
|
作者
Carobene, Anna [1 ]
Cabitza, Federico [2 ,3 ]
Bernardini, Sergio [4 ,5 ]
Gopalan, Raj [6 ]
Lennerz, Jochen K. [7 ]
Weir, Clare [8 ]
Cadamuro, Janne [9 ]
机构
[1] IRCCS San Raffaele Sci Inst, Lab Med, Via Olgettina 60, I-20132 Milan, Italy
[2] IRCCS Osped Galeazzi SantAmbrogio, Milan, Italy
[3] Univ Milano Bicocca, DISCo, Milan, Italy
[4] Tor Vergata Univ Hosp, Unit Lab Med, Rome, Italy
[5] Univ Tor Vergata, Dept Expt Med, Rome, Italy
[6] Siemens Healthineers, Siemens Healthcare Diagnost, Malvern, PA USA
[7] Harvard Med Sch, Ctr Integrated Diagnost, Massachusetts Gen Hosp, Dept Pathol, Boston, MA USA
[8] Sysmex Europe SE, Norderstedt, Germany
[9] Paracelsus Med Univ Salzburg, Dept Lab Med, Salzburg, Austria
关键词
artificial intelligence; laboratory medicine; machine learning; performance metrics; robustness;
D O I
10.1515/cclm-2022-1030
中图分类号
R446 [实验室诊断]; R-33 [实验医学、医学实验];
学科分类号
1001 ;
摘要
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.
引用
收藏
页码:535 / 543
页数:9
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  • [1] Where is laboratory medicine headed in the next decade? Partnership model for efficient integration and adoption of artificial intelligence into medical laboratories (vol 61, pg 535, 2022)
    Carobene, Anna
    Cabitza, Federico
    Bernardini, Sergio
    Gopalan, Raj
    Lennerz, Jochen K.
    Weir, Clare
    Cadamuro, Janne
    CLINICAL CHEMISTRY AND LABORATORY MEDICINE, 2023, 61 (07) : 1359 - 1359