ReviewClinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases2020
Digital microbiology.
Review in Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed.
- A custom GPT-based model for the automated analysis and interpretation of antimicrobial susceptibility tests in Gram-negative bacteria.JAC-antimicrobial resistance · 2026Article
- Uncloaking the black-box: the need for explainable artificial intelligence in clinical microbiology and infectious diseases applications.Frontiers in public health · 2026Review
- Correlation of broad-spectrum antibiotic utilization patterns with invasive candidiasis in critically ill patients and development of an early prediction model.Scientific reports · 2025Article
- Implementation of convolutional neural networks for microbial colony recognition.Microbiology spectrum · 2025Article
- Distribution of Antimicrobial Resistance and Biofilm Production Genes in the Genomic Sequences ofAntibiotics (Basel, Switzerland) · 2025Article
- Artificial Intelligence in Biomedical Sciences: A Scoping Review.British journal of biomedical science · 2025Article
- Can Artificial Intelligence Enhance the Antimicrobial Effectiveness of Photodynamic Therapy? What We Know and What We Do Not Know?Journal of lasers in medical sciences · 2025Review
- A Critical Review of the Prospect of Integrating Artificial Intelligence in Infectious Disease Diagnosis and Prognosis.Interdisciplinary perspectives on infectious diseases · 2025Review
- GPT-4-based AI agents-the new expert system for detection of antimicrobial resistance mechanisms?Journal of clinical microbiology · 2024Article
- ChatGPT, GPT-4, and Other Large Language Models: The Next Revolution for Clinical Microbiology?Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2023Article
- Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology.Nature communications · 2023Article
- Machine Learning with Alpha Toxin Phenotype to Predict Clinical Outcome in Patients withToxins · 2023Article
- Image dataset of urine test results on petri dishes for deep learning classification.Data in brief · 2023Article
- The pre-analytical process management status and influencing factors of laboratory test before prescribing antimicrobial in developing country.BMC health services research · 2023Article
- Review
- Galaxy ASIST: A web-based platform for mapping and assessment of global standards of antimicrobial susceptibility: A case study inFrontiers in microbiology · 2022Article
- Future developments in training.Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases · 2021Review
- State-of-the-Art Techniques for Diagnosis of Medical Parasites and Arthropods.Diagnostics (Basel, Switzerland) · 2021Review
- Next-generation sequencing in routine clinical microbiology and infectious diseases: an ESGMD-ESGEM ESCMID postgraduate course.New microbes and new infectionsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDigitalization and artificial intelligence have an important impact on the way microbiology laboratories will work in the near future. Opportunities and challenges lie ahead to digitalize the microbiological workflows. Making efficient use of big data, machine learning, and artificial intelligence in clinical microbiology requires a profound understanding of data handling aspects.
objectiveThis review article summarizes the most important concepts of digital microbiology. The article gives microbiologists, clinicians and data scientists a viewpoint and practical examples along the diagnostic process. SOURCES: We used peer-reviewed literature identified by a PubMed search for digitalization, machine learning, artificial intelligence and microbiology. CONTENT: We describe the opportunities and challenges of digitalization in microbiological diagnostic processes with various examples. We also provide in this context key aspects of data structure and interoperability, as well as legal aspects. Finally, we outline the way for applications in a modern microbiology laboratory. IMPLICATIONS: We predict that digitalization and the usage of machine learning will have a profound impact on the daily routine of laboratory staff. Along the analytical process, the most important steps should be identified, where digital technologies can be applied and provide a benefit. The education of all staff involved should be adapted to prepare for the advances in digital microbiology.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.