Editorial: Artificial intelligence in microbial and microscopic analysis
Abstract
Artificial intelligence in microbial and microscopic analysisArtificial intelligence (AI) is transforming the way researchers observe, quantify, and interpret microbial and microscopic systems.Microscopy, sequencing, physiological signals, clinical records, and multi-source biomedical data now provide increasingly rich descriptions of biological and clinical processes.However, their complexity also challenges conventional manual or rule-based analysis.AI-driven methods offer new opportunities to extract discriminative features, integrate heterogeneous information, improve diagnostic and monitoring workflows, and generate reproducible evidence across microbiology, microscopic imaging, bioinformatics, and clinical decision support.The Research Topic "Artificial intelligence in microbial and microscopic analysis" was launched to collect methodological advances and practical applications that connect AI with microbial, microscopic, and biomedical analysis.The 15 articles in this Research Topic demonstrate the breadth of this interdisciplinary field, spanning environmental microorganism recognition, antimicrobial resistance surveillance, microbiome-based disease prediction, medical microscopic image analysis, multimodal single-cell analysis, physiological signal interpretation, precision measurement, and domain-specific AI systems.Together, these contributions show how AI can support both biological discovery and clinically oriented decision-making.A significant portion of the…
Read the paper
Related studies
- Knowledge Management Perspective of Generative Artificial Intelligence
- Navigating Generative Artificial Intelligence Promises and Perils for Knowledge and Creative Work
- Characterizing the evolving research on enterprise content management
- Large language models and multimodal foundation models for precision oncology
- From Generative AI to Extended Reality: Multidisciplinary Perspectives on the Challenges, Opportunities, and Future of Educational Computing
- Generative Artificial Intelligence: A New Engine for Advancing Environmental Science and Engineering
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.