Artificial Intelligence in the pre-analytical phase: state-of-the art and future perspectives

Artificial Intelligence in the pre-analytical phase

  • Prof. Giuseppe Lippi Section of Clinical Biochemistry and School of Medicine, University of Verona, Verona, Italy https://orcid.org/0000-0001-9523-9054
  • Dr. Camilla Mattiuzzi Medical Direction, Hospital of Rovereto, Provincial Agency for Social and Sanitary Services (APSS), Trento, Italy https://orcid.org/0000-0002-5902-2525
  • Dr. Emmanuel J. Favaloro Department of Haematology, Sydney Centres for Thrombosis and Haemostasis, Institute of Clinical Pathology and Medical Research (ICPMR), NSW Health Pathology, Westmead Hospital, Westmead, NSW Australia; School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Westmead Hospital, Westmead, New South Wales, Australia; Faculty of Science and Health, Charles Sturt University, Wagga Wagga, NSW, Australia. https://orcid.org/0000-0002-2103-1661
Keywords: Artificial Intelligence, Robotics, Preanalytical phase, Preanalytical variability, Errors

Abstract


The use of artificial intelligence (AI) has become widespread in many areas of science and medicine, including laboratory medicine. Although it seems obvious that the analytical and post-analytical phases could be the most important fields of application in laboratory medicine, a kaleidoscope of new opportunities has emerged to extend the benefits of AI to many manual labor-intensive activities belonging to the pre-analytical phase, which are inherently characterized by enhanced vulnerability and higher risk of errors. These potential applications involve increasing the appropriateness of test prescription (with computerized physician order entry or demand management tools), improved specimen collection (using active patient recognition, automated specimen labeling, vein recognition and blood collection assistance, along with automated blood drawing), more efficient sample transportation (facilitated by the use of pneumatic transport systems or drones, and monitored with smart blood tubes or data loggers), systematic evaluation of sample quality (by measuring serum indices, fill volume or for detecting sample clotting), as well as error detection and analysis. Therefore, this opinion paper aims to discuss the state-of-the-art and some future possibilities of AI in the preanalytical phase.

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Published
2023/08/29
Section
Oppinion paper