Jul 2026· Journal of Microbiological Methods· Vol 248, pp.
107638
· 0 citations· 137 references
Medicine
TL;DR
This review comprehensively evaluates current AI applications in microbiology, highlighting their advantages, limitations, and implementation challenges and examines the suitability of different AI methodologies for specific laboratory tasks.
Abstract
Microbiology laboratories play a critical role in the diagnosis and management of infectious diseases. However, recent advancements aimed at reducing human workload and minimizing time loss are gaining popularity. Artificial intelligence (AI) technologies, particularly machine learning (ML) and deep learning (DL), have been reported to contribute significantly to microbial laboratory diagnostics. Through this approach, molecular methods, genetic sequencing, microbiological meta-analyses, and related fields benefit from faster and more accurate analytic capabilities. In addition to diagnostic applications, AI is increasingly used in genomics, metagenomics, antimicrobial resistance (AMR) prediction, and drug and vaccine discovery, enabling more comprehensive and data-driven microbiological analysis. This review comprehensively evaluates current AI applications in microbiology, highlighting their advantages, limitations, and implementation challenges. It further examines the suitability of different AI methodologies for specific laboratory tasks and compares AI-driven approaches with conventional expert-based practices. Finally, the study emphasizes the complementary roles of AI systems and human expertise, underscoring their synergistic potential to improve diagnostic accuracy, efficiency, and clinical decision-making.
Background: Artificial Intelligence (AI) and Machine Learning (ML) are rapidly transforming the field of microbiology by improving the accuracy, speed, and efficiency of disease diagnosis, microbial identification, antimicrobial susceptibility testing, and outbreak surveillance. These advanced technologies facilitate the analysis of large microbiological data sets and support clinical decision- making, thereby enhancing patient care and public health outcomes.Objective: To review the applications, advantages, and future prospects of AI and Machine Learning in microbiology and their role in improving diagnostic and research capabilities.Methods: A comprehensive review of published literature was conducted using scientific databases, including PubMed, Google Scholar, and Scopus. Relevant articles focusing on AI and ML applications in clinical microbiology, diagnostic microbiology, antimicrobial resistance detection, epidemiological surveillance, and laboratory automation were analyzed.Results: AI and ML have demonstrated significant potential in various microbiological applications. These technologies enable rapid identification of microorganisms through image analysis, automated interpretation of culture plates, molecular diagnostics, genomic data analysis, and prediction of antimicrobial resistance patterns. AI-based systems have improved the detection of infectious diseases such as tuberculosis, COVID-19, sepsis, and bloodstream infections. Furthermore, machine learning algorithms facilitate outbreak prediction, disease surveillance, and personalized treatment strategies. The integration of AI with laboratory information systems has also enhanced workflow efficiency and reduced human errors.Conclusion: Artificial Intelligence (AI) and Machine Learning (ML) are transforming modern microbiology by enabling rapid, precise, and cost-effective diagnostic approaches. These technologies play a significant role in microbial identification, prediction of antimicrobial resistance, disease surveillance, and outbreak detection. Their integration into microbiological practices has the potential to enhance healthcare services, support clinical decision-making, and improve patient outcomes. With ongoing technological advancements, proper validation, and adherence to ethical standards, AI and ML are expected to have an increasingly important role in both clinical and research microbiology in the future.
Unknown authors· Central India Journal of Med...· 0 citations
Background and Objectives: Antimicrobial resistance (AMR) is a major challenge, particularly in intensive care units, where broad-spectrum therapy is often initiated before microbiological confirmation. Artificial intelligence (AI) may improve AMR prediction, but its clinical value depends on integration with bioengineering-enabled digital microbiology. This narrative review examines how AI, bioengineering platforms and digital microbiology can support AMR prediction, clinical decision support and antimicrobial stewardship across the sample-to-decision pipeline. Materials and Methods: A targeted narrative review was conducted using PubMed/MEDLINE and Google Scholar. Publications from 2020 onward were prioritized, while earlier seminal studies, methodological frameworks and regulatory documents were included when relevant. Evidence was synthesized across AI-based resistance prediction, antimicrobial stewardship, digital microbiology and bioengineering technologies. Results: AI and machine-learning approaches showed promising performance in patient-level resistance prediction, pathogen-level susceptibility prediction and antimicrobial stewardship. For example, model discrimination reached an AUROC of 0.936 for carbapenem-resistant Klebsiella pneumoniae prediction, while model-guided empirical therapy in Enterobacterales bloodstream infections could have increased active beta-lactam therapy from 70% to 79%. However, most evidence remains retrospective and single-centre, with limited external or prospective validation. Conclusions: AI has considerable potential to support AMR prediction and antimicrobial stewardship, but current evidence primarily demonstrates technical feasibility rather than established clinical effectiveness. Broader implementation will require rigorous validation, integration into clinical workflows, continuous monitoring and demonstration of clinical benefit.
Oana Frandeș, Leonard Azamfirei, Oana-Elena Branea et al.· Medicina· 0 citations
BACKGROUND
Clinical microbiology is undergoing rapid transformation driven by modern technologies generating high-volume, high-dimensional, and heterogeneous datasets that exceed the analytical capabilities of traditional rule-based approaches. Artificial intelligence (AI) provides powerful computational methods to gain diagnostic, biological, and epidemiological insights from these complex data.
SOURCES
This narrative review synthesizes information from peer-reviewed literature in clinical microbiology and machine learning, including applied research articles and relevant guidelines on AI development, evaluation, and implementation in healthcare.
OBJECTIVES
This narrative review provides a structured introduction to the machine learning (ML) lifecycle from the perspective of clinical microbiology, outlining the sequence of steps in data preparation, model development, evaluation, and deployment.
CONTENT
We describe the characteristics of modern microbiology datasets and emphasize the importance of rigorous problem definition, data integration, quality assessment, and feature engineering. Model development considerations are summarized for supervised learning, including hyperparameter optimization, model choice, and multimodal data integration. Evaluation frameworks are examined with attention to typical challenges for microbiology applications, including class imbalance, generalization, robustness, model interpretation, and explainability. Finally, we summarize key elements of model deployment, including reproducible packaging, integration with Laboratory Information Systems and Electronic Medical Record systems, MLOps practices, ongoing drift monitoring, regulatory and governance requirements.
IMPLICATIONS
Successful AI implementation in microbiology demands alignment with laboratory workflows, transparency and interpretability of model behavior, robust performance under real-world variability, and strong data governance. Addressing these factors is essential for translating promising methodological advances into solutions to enhance diagnostics, antimicrobial stewardship, and infection prevention.
Imane Lboukili, Benjamin R McFadden, Tavpritesh Sethi et al.· Clinical Microbiology and In...· 0 citations
Background Artificial intelligence (AI) is transforming haematology diagnostics by improving accuracy, efficiency, and reproducibility in workflows traditionally reliant on manual microscopy and expert interpretation. Integrating AI into laboratory medicine presents opportunities to enhance diagnostic precision and reduce variability, particularly in resource-limited settings. Aim This narrative review examines the application of AI across major domains of haematology, morphological diagnosis, flow cytometry, cytogenetics, genomics, and clinical decision support, while addressing ethical, regulatory, and economic considerations relevant to global and African laboratory contexts. Methods A comprehensive literature search of PubMed, Scopus, Web of Science, and EMBASE identified studies describing or evaluating AI algorithms in haematologic diagnostics, focusing on model performance, validation level, and clinical applicability. Results Recent studies demonstrate strong performance of deep learning models, particularly convolutional neural networks and hybrid convolutional neural networks–transformer architectures, in automating blood and bone marrow morphology, detecting subtle dysplastic changes, and supporting digital workflows. In flow cytometry, AI enhances automated gating and rare event detection, while cytogenetic and genomic tools aid in karyotyping, variant classification, and structural abnormality recognition. Decision-support systems further assist in diagnostic triage and treatment planning. However, widespread implementation is limited by data heterogeneity, inadequate multicentre validation, and evolving ethical and regulatory frameworks. Conclusion Advancing AI integration in haematology will require robust validation studies, explainable AI approaches, and equitable adoption strategies. Strengthening African laboratory systems through such innovations offers a pathway toward improved diagnostic capacity and sustainable digital transformation. What this study adds The current study highlighted that AI is improving haematology laboratory tasks such as cell identification, differential diagnosis assistance, and result interpretation, but still faces challenges in validation, bias, and workflow integration. Future progress depends on stronger external validation, better explainable models, and closer collaboration between laboratory experts and AI specialists.
H. Osman· African Journal of Laborator...· 0 citations
Abstract Antimicrobial resistance (AMR) poses a significant global public health threat, and efforts to mitigate it have been aided by artificial intelligence (AI) methods. Key areas of applications include rapid diagnostics, drug discovery and repurposing, surveillance and predictive modelling, and antibiotic stewardship. This review aims to summarize key literature on AI applications for mitigating AMR including original research articles from PubMed and Scopus published from October 2024 to 2025 to summarize and find out the way ahead for successful application of AI. The key search terms included were AMR, AI and applications such as diagnostics, drug discovery and repurposing, surveillance, predictive modelling and stewardship. The data used for these applications, the techniques applied and the predictive targets have undergone significant expansion, along with the focus on model deployment, external validation and model interpretability. Although more complex models, such as neural networks and transformers, have been experimented with, classical machine learning (ML) models still dominate the AMR prediction space, while large language models are also being tested for prediction, as well as antibiotic stewardship. For drug discovery, data mining for antimicrobial peptides from different sources is a major application. Predictive modelling using next-generation sequencing data has been the most studied. The application of AI/ML to large and complex data from multiple sources could provide a promising arena for developing clinically translational tools. With more data availability, regulatory measures, real-world validation and transparency, there is scope for responsibly integrating innovative technology into clinical practice.
Swetha Valavarasu, S. Marathe, Sanjay Kochar et al.· JAC-Antimicrobial Resistance· 0 citations
Introduction: The digital transformation of healthcare is accelerating, driven by unprecedented advancements in Artificial Intelligence (AI). From large language models (LLMs) to biomolecular structure prediction, AI is redefining modern diagnostic and therapeutic standards.
Aim: This review evaluates the current state of AI applications in medicine, focusing on clinical knowledge encoding, molecular drug discovery, and administrative workflow optimization, while critically addressing the technical, ethical, and systemic challenges of their institutional implementation.
Materials and Methods: A structured analysis was conducted utilizing a hybrid approach that combines a multi-decade bibliometric trend perspective with a detailed synthesis of 21 landmark publications, clinical trials, and meta-analyses from high-impact journals.
Results: AI demonstrates expert-level performance in medical knowledge retrieval and spatiotemporal diagnostics. AlphaFold 3 has revolutionized computational therapeutics through all-atom biomolecular interaction prediction, while ambient AI scribes significantly reduce physician burnout by automating clinical documentation workflows. However, data-driven "hallucinations" in LLMs and the inherent "black box" nature of deep learning architectures remain critical barriers to autonomous deployment.
Conclusions: AI is successfully transitioning from an isolated research tool into an essential clinical "co-pilot." Achieving its full potential in Medicine 4.0 requires robust frameworks for algorithmic explainability, global dataset diversification, and a strategic synergy between machine precision and human clinical judgment.
Aleksandra Stańczyk, Kinga Haduch, Zuzanna Michalska et al.· International Journal of Inn...· 0 citations
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