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

Author:

Carobene Anna1,Cabitza Federico23,Bernardini Sergio45,Gopalan Raj6,Lennerz Jochen K.7,Weir Clare8,Cadamuro Janne9ORCID

Affiliation:

1. IRCCS San Raffaele Scientific Institute , Milan , Italy

2. IRCCS Ospedale Galeazzi - Sant’Ambrogio , Milan , Italy

3. DISCo, Università Degli Studi di Milano-Bicocca , Milan , Italy

4. Unit of Laboratory Medicine, Tor Vergata University Hospital , Rome , Italy

5. Department of Experimental Medicine , University of Tor Vergata , Rome , Italy

6. Siemens Healthcare Diagnostics, Siemens Healthineers , Malvern , PA , USA

7. Department of Pathology , Center for Integrated Diagnostics, Harvard Medical School, Massachusetts General Hospital , Boston , MA , USA

8. Sysmex Europe SE , Norderstedt , Germany

9. Department of Laboratory Medicine , Paracelsus Medical University Salzburg , Salzburg , Austria

Abstract

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.

Publisher

Walter de Gruyter GmbH

Subject

Biochemistry (medical),Clinical Biochemistry,General Medicine

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