Computer Vision and Artificial Intelligence Are Emerging Diagnostic Tools for the Clinical Microbiologist

Area of Science:

• Clinical microbiology informatics
• Medical diagnostics
• Artificial intelligence applications

Background:

• Artificial intelligence (AI) is gaining importance in clinical microbiology informatics.
• AI-based testing promises improvements in turnaround time, quality, and cost.
• Existing AI applications, like computer vision by Mathison et al., highlight potential but also unmet needs.

Purpose of the Study:

• To explore the potential of AI applications within the clinical microbiology laboratory.
• To identify large datasets amenable to AI diagnostic development.
• To emphasize the need for clinical microbiologists to engage with AI and computer vision.

Main Methods:

• Review of current AI applications in clinical microbiology.
• Identification of data types suitable for AI development (genomics, metagenomics, mass spectra, digital images).
• Discussion of a specific computer vision AI study (Mathison et al.).

Main Results:

• Clinical microbiology possesses large datasets (genomic, metagenomic, spectral, imaging) suitable for AI diagnostics.
• Computer vision is one AI modality with demonstrated application in microbiology.
• Opportunities exist for broader AI integration beyond current applications.

Conclusions:

• AI, particularly computer vision, presents emerging tools for clinical microbiology.
• Clinical microbiologists must study, develop, and implement AI to enhance diagnostic capabilities.
• Further AI integration can significantly improve clinical microbiology services.