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A comprehensive survey of artificial intelligence adoption in European laboratory medicine: current utilization and prospects

J. Cadamuro, A. Carobene, F. Cabitza, Z. Debeljak, S. De Bruyne, W. van Doorn, E. Johannes, G. Frans, H. Özdemir, S. Martin Perez, D. Rajdl, A. Tolios, A. Padoan, European Federation of Clinical Chemistry and Laboratory Medicine Working Group on...

. 2025 ; 63 (4) : 692-703. [pub] 20241024

Language English Country Germany

Document type Journal Article

Persistent link   https://www.medvik.cz/link/bmc25009434

BACKGROUND: As the healthcare sector evolves, Artificial Intelligence's (AI's) potential to enhance laboratory medicine is increasingly recognized. However, the adoption rates and attitudes towards AI across European laboratories have not been comprehensively analyzed. This study aims to fill this gap by surveying European laboratory professionals to assess their current use of AI, the digital infrastructure available, and their attitudes towards future implementations. METHODS: We conducted a methodical survey during October 2023, distributed via EFLM mailing lists. The survey explored six key areas: general characteristics, digital equipment, access to health data, data management, AI advancements, and personal perspectives. We analyzed responses to quantify AI integration and identify barriers to its adoption. RESULTS: From 426 initial responses, 195 were considered after excluding incomplete and non-European entries. The findings revealed limited AI engagement, with significant gaps in necessary digital infrastructure and training. Only 25.6 % of laboratories reported ongoing AI projects. Major barriers included inadequate digital tools, restricted access to comprehensive data, and a lack of AI-related skills among personnel. Notably, a substantial interest in AI training was expressed, indicating a demand for educational initiatives. CONCLUSIONS: Despite the recognized potential of AI to revolutionize laboratory medicine by enhancing diagnostic accuracy and efficiency, European laboratories face substantial challenges. This survey highlights a critical need for strategic investments in educational programs and infrastructure improvements to support AI integration in laboratory medicine across Europe. Future efforts should focus on enhancing data accessibility, upgrading technological tools, and expanding AI training and literacy among professionals. In response, our working group plans to develop and make available online training materials to meet this growing educational demand.

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$a A comprehensive survey of artificial intelligence adoption in European laboratory medicine: current utilization and prospects / $c J. Cadamuro, A. Carobene, F. Cabitza, Z. Debeljak, S. De Bruyne, W. van Doorn, E. Johannes, G. Frans, H. Özdemir, S. Martin Perez, D. Rajdl, A. Tolios, A. Padoan, European Federation of Clinical Chemistry and Laboratory Medicine Working Group on Artificial Intelligence
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$a BACKGROUND: As the healthcare sector evolves, Artificial Intelligence's (AI's) potential to enhance laboratory medicine is increasingly recognized. However, the adoption rates and attitudes towards AI across European laboratories have not been comprehensively analyzed. This study aims to fill this gap by surveying European laboratory professionals to assess their current use of AI, the digital infrastructure available, and their attitudes towards future implementations. METHODS: We conducted a methodical survey during October 2023, distributed via EFLM mailing lists. The survey explored six key areas: general characteristics, digital equipment, access to health data, data management, AI advancements, and personal perspectives. We analyzed responses to quantify AI integration and identify barriers to its adoption. RESULTS: From 426 initial responses, 195 were considered after excluding incomplete and non-European entries. The findings revealed limited AI engagement, with significant gaps in necessary digital infrastructure and training. Only 25.6 % of laboratories reported ongoing AI projects. Major barriers included inadequate digital tools, restricted access to comprehensive data, and a lack of AI-related skills among personnel. Notably, a substantial interest in AI training was expressed, indicating a demand for educational initiatives. CONCLUSIONS: Despite the recognized potential of AI to revolutionize laboratory medicine by enhancing diagnostic accuracy and efficiency, European laboratories face substantial challenges. This survey highlights a critical need for strategic investments in educational programs and infrastructure improvements to support AI integration in laboratory medicine across Europe. Future efforts should focus on enhancing data accessibility, upgrading technological tools, and expanding AI training and literacy among professionals. In response, our working group plans to develop and make available online training materials to meet this growing educational demand.
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$a Carobene, Anna $u Laboratory Medicine, IRCCS San Raffaele Scientific Institute, Milan, Italy
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$a Debeljak, Zeljko $u Clinical Institute of Laboratory Diagnostics, University Hospital Centre Osijek, Osijek, Croatia $u Department of Pharmacology, JJ Strossmayer University of Osijek, Osijek, Croatia
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$a De Bruyne, Sander $u Department of Diagnostic Sciences, Ghent University, Ghent, Belgium $u Department of Laboratory Medicine, AZ Sint-Blasius, Dendermonde, Belgium
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$a van Doorn, William $u Central Diagnostic Laboratory, Department of Clinical Chemistry, Maastricht University Medical Center +, Maastricht, The Netherlands
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$a Johannes, Elias $u MDI Limbach Berlin GmbH, Berlin, Germany $u HMU Health and Medical University GmbH, Potsdam, Germany
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$a Özdemir, Habib $u Faculty of Medicine, Department of Medical Biochemistry, Manisa Celal Bayar University, Manisa, Türkiye $u Türkiye Institutes of Health, Türkiye Health Data Research and Artificial Intelligence Applications Institute, Istanbul, Türkiye $1 https://orcid.org/0000000212677233
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$a Martin Perez, Salomon $u Laboratory Medicine Department, Virgen Macarena University Hospital, Seville, Spain $1 https://orcid.org/0000000220863597
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$a Rajdl, Daniel $u Medical Faculty in Pilsen, Charles University, Pilsen, Czech Republic
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$a Tolios, Alexander $u Department of Transfusion Medicine and Cell Therapy, Medical University of Vienna, Vienna, Austria
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