ArticleEuropean journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology2026
Artificial intelligence in clinical microbiology: results from the first National survey by the Italian association of clinical microbiologists.
Article in European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The trial behind it
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Who cites it
3 citing papers in PubMed.
- Deep Learning-Based Malaria Parasite Image Classification on Real Microscopy Data.Infectious disease reports · 2026Article
- Conditional Acceptance and the Optimism-Knowledge Gap: A Scoping Review of Attitudes and Perceptions of Artificial Intelligence in Healthcare in Italy.Medical sciences (Basel, Switzerland) · 2026Article
- European Society of Clinical Microbiology and Infectious Diseases Study Group for Artificial Intelligence and Digitalisation consensus statements on key aspects in the interaction of clinical microbiologists and infectious disease specialists with general-purpose large language model-based systems: project protocol.Therapeutic advances in infectious diseaseArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeArtificial Intelligence (AI) and Machine Learning (ML) are innovative technologies, gathering particular interest in all laboratory settings. The aim of this study was to assess the current state of AI and ML adoption in Italian laboratories of clinical microbiology.
methodsA structured 63-question survey was developed by the AI/ML in Microbiology Study Group and distributed to all members of the Italian Association of Clinical Microbiologists between December 2024 and March 2025. Responses were collected anonymously and analyzed using descriptive statistics analysis.
resultsA total of 163 professionals completed the survey. While 25.4% reported current AI/ML usage-primarily in bacteriology and virology-the majority had limited experience with AI technologies. Only 13.6% of respondents had a good understanding of AI/ML concepts, and 2.5% reported having trained data scientists on staff. Major barriers included lack of trained personnel and insufficient infrastructure. Most participants (99.0%) expressed interest in targeted AI training, and 57.5% showed willingness to collaborate on AI-related initiatives. Large language models (LLMs) were seen as promising, especially for data interpretation, despite low adoption rates.
conclusionThe survey might provide valuable insights to identify priority areas for intervention, guide future training initiatives and develop targeted strategies to promote the adoption of these technologies through a fruitful dialogue with companies and IT professionals.
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Registered trials
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