ReviewFrontiers in microbiology2024
Artificial intelligence applications in the diagnosis and treatment of bacterial infections.
Review in Frontiers in microbiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
What it found
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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
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for infection surveillance, risk stratification, and antimicrobial decision support in acute-care hospitals: a scoping review.Frontiers in artificial intelligence · 2026Pooled it
- Innovative Applications of Artificial Intelligence in Bacteriophage Research: A New Chapter in Future Medicine.Microorganisms · 2026Review
- Balancing Cationicity and Hydrophobicity in Dermaseptin-A4 Generates a Selective Antimicrobial Peptide with Enhanced Therapeutic Potential.Antibiotics (Basel, Switzerland) · 2026Article
- Metabolomics and Artificial Intelligence (AI) assisted metabolomics in the diagnosis of non-tuberculous mycobacterial infections: progress and challenges.New microbes and new infections · 2026Review
- Machine learning-driven decision support for antibiotic optimization in typhoid fever based on patient profiles.BMC medical informatics and decision making · 2026Article
- Artificial Intelligence-Assisted Pathogen Detection: Algorithms, Biosensing Platforms, and Applications.Biosensors · 2026Review
- Peering Inside the Black Box: Explainable AI to Interpret Advanced Computer Vision Fungal Pathogen Prediction.Scientific reports · 2026Article
- Methicillin-ResistantMicroorganisms · 2026Review
- A machine learning model for accurate prediction of CMV end-organ disease.BMC medical informatics and decision making · 2026Article
- Advancements in Artificial Intelligence-Based Diagnostic Tools Used to Detect Fungal Infections: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Study on drug resistance, biofilm formation, phylogenetic and virulence gene analysis of Escherichia coli from diarrheic lambs.BMC veterinary research · 2026Article
- Comprehensive Approaches to Protein Detection and Analysis in Mycobacterium tuberculosis.Advances in experimental medicine and biology · 2026Review
- Advances in AI for detecting pulmonary inflammation and perioperative medicine: a mini-review.Frontiers in medicine · 2026Review
- Next-generation viral detection through AI-enhanced nanotechnology: advances, challenges, and future directions.Frontiers in molecular biosciences · 2026Article
- Application of artificial intelligence in geriatric infection: recent advances and prospects.Frontiers in cellular and infection microbiology · 2026Review
- Mapping the landscape of AI and ML in vaccine innovation: A bibliometric study.Human vaccines & immunotherapeutics · 2025Article
- Label-free phenotypic antimicrobial susceptibility testing on microfluidic platforms: a review of advances and translation.Mikrochimica acta · 2025Review
- Precise detection of Eimeria oocysts in sheep: a deep learning model based on microscopic images.Parasites & vectors · 2025Article
- Leveraging artificial intelligence for One Health: opportunities and challenges in tackling antimicrobial resistance - scoping review.One health outlook · 2025Article
- Role of artificial intelligence in ICU therapeutic decision-making for severe infections.Current opinion in critical care · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The diagnosis and treatment of bacterial infections in the medical and public health field in the 21st century remain significantly challenging. Artificial Intelligence (AI) has emerged as a powerful new tool in diagnosing and treating bacterial infections. AI is rapidly revolutionizing epidemiological studies of infectious diseases, providing effective early warning, prevention, and control of outbreaks. Machine learning models provide a highly flexible way to simulate and predict the complex mechanisms of pathogen-host interactions, which is crucial for a comprehensive understanding of the nature of diseases. Machine learning-based pathogen identification technology and antimicrobial drug susceptibility testing break through the limitations of traditional methods, significantly shorten the time from sample collection to the determination of result, and greatly improve the speed and accuracy of laboratory testing. In addition, AI technology application in treating bacterial infections, particularly in the research and development of drugs and vaccines, and the application of innovative therapies such as bacteriophage, provides new strategies for improving therapy and curbing bacterial resistance. Although AI has a broad application prospect in diagnosing and treating bacterial infections, significant challenges remain in data quality and quantity, model interpretability, clinical integration, and patient privacy protection. To overcome these challenges and, realize widespread application in clinical practice, interdisciplinary cooperation, technology innovation, and policy support are essential components of the joint efforts required. In summary, with continuous advancements and in-depth application of AI technology, AI will enable doctors to more effectivelyaddress the challenge of bacterial infection, promoting the development of medical practice toward precision, efficiency, and personalization; optimizing the best nursing and treatment plans for patients; and providing strong support for public health safety.
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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.