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ESCMID workshop: Artificial intelligence and machine learning in medical microbiology diagnostics.
Mariella Greutmann1, Karsten Borgwardt2, Sarah Brüningk3
1Institute of Medical Microbiology, University of Zurich, Zurich, Switzerland.
Microbes and Infection
|September 7, 2025
Summary
Artificial intelligence (AI) and machine learning (ML) can revolutionize medical microbiology diagnostics. The ESCMID workshop highlighted the need for collaboration, education, and investment to ensure equitable AI implementation in clinical settings.
Area of Science:
- Medical Microbiology
- Artificial Intelligence
- Machine Learning
Background:
- Rapid advancements in AI and ML present opportunities for medical microbiology diagnostics.
- Challenges include data interoperability, quality control, and ethical considerations.
Purpose of the Study:
- To explore AI and ML applications in medical microbiology diagnostics.
- To address challenges and foster collaboration in the field.
Main Methods:
- Expert lectures and practical sessions on AI/ML concepts and applications.
- Panel discussions on regulatory compliance, ethics, equity, and sustainability.
- Hands-on experience with AI tools and platforms.
Main Results:
- Key applications discussed: whole-genome sequencing for antimicrobial resistance, AI-enhanced digital microscopy, and MALDI-TOF MS diagnostics.
- Emphasis on standardized protocols, regulatory compliance, and ethical considerations.
- Identified critical issues: equity, global disparities, sustainability, and environmental impact.
Conclusions:
- AI and ML hold significant potential for improving pathogen identification, antimicrobial susceptibility prediction, and outbreak detection.
- Interdisciplinary collaboration, continuous education, and investment in equitable AI infrastructure are crucial.
- Realizing AI's full potential in clinical diagnostics requires addressing ethical and global disparities.
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