Janne Cadamuro, Anna Carobene, Federico Cabitza, Zeljko Debeljak, Sander De Bruyne, William van Doorn, Elias Johannes, Glynis Frans, Habib Özdemir, Salomon Martin Perez, Daniel Rajdl, Alexander Tolios, Andrea Padoan
{"title":"A comprehensive survey of artificial intelligence adoption in European laboratory medicine: current utilization and prospects.","authors":"Janne Cadamuro, Anna Carobene, Federico Cabitza, Zeljko Debeljak, Sander De Bruyne, William van Doorn, Elias Johannes, Glynis Frans, Habib Özdemir, Salomon Martin Perez, Daniel Rajdl, Alexander Tolios, Andrea Padoan","doi":"10.1515/cclm-2024-1016","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":10390,"journal":{"name":"Clinical chemistry and laboratory medicine","volume":" ","pages":""},"PeriodicalIF":3.8000,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical chemistry and laboratory medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1515/cclm-2024-1016","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MEDICAL LABORATORY TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
Clinical Chemistry and Laboratory Medicine (CCLM) publishes articles on novel teaching and training methods applicable to laboratory medicine. CCLM welcomes contributions on the progress in fundamental and applied research and cutting-edge clinical laboratory medicine. It is one of the leading journals in the field, with an impact factor over 3. CCLM is issued monthly, and it is published in print and electronically.
CCLM is the official journal of the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) and publishes regularly EFLM recommendations and news. CCLM is the official journal of the National Societies from Austria (ÖGLMKC); Belgium (RBSLM); Germany (DGKL); Hungary (MLDT); Ireland (ACBI); Italy (SIBioC); Portugal (SPML); and Slovenia (SZKK); and it is affiliated to AACB (Australia) and SFBC (France).
Topics:
- clinical biochemistry
- clinical genomics and molecular biology
- clinical haematology and coagulation
- clinical immunology and autoimmunity
- clinical microbiology
- drug monitoring and analysis
- evaluation of diagnostic biomarkers
- disease-oriented topics (cardiovascular disease, cancer diagnostics, diabetes)
- new reagents, instrumentation and technologies
- new methodologies
- reference materials and methods
- reference values and decision limits
- quality and safety in laboratory medicine
- translational laboratory medicine
- clinical metrology
Follow @cclm_degruyter on Twitter!