AI and Digital Diagnostics in Mycology
How digital microscopy and machine learning are being explored for fungal identification, and how to evaluate these tools responsibly.
An overview of what you will study and build.
AI and Digital Diagnostics in Mycology explores how digital microscopy and machine learning are being investigated to support fungal identification, and how to judge these tools responsibly. Students begin with digital microscopy and whole-slide imaging, then learn the fundamentals of machine learning in plain terms suited to laboratory professionals. The course examines image-based identification of fungi, AI applied to MALDI-TOF spectra and sequence data, and the critical issues of validation, bias and regulation before considering how to introduce digital tools into practice.
Many AI tools for mycology remain experimental, so the ability to read performance claims critically is as important as technical knowledge. This course suits laboratory scientists and health data professionals. No programming experience is required. Tutors work at the interface of laboratory mycology and data science. For the final project, students write an evaluation framework for a digital or AI tool in a mycology laboratory, covering intended use, data requirements, validation design, risks and implementation steps.
6 modules, one growing line of knowledge.
Hover or tap a module to follow the path from the first session to the final project. Open a module to see the topics it covers.
- Digital image capture
- Whole-slide scanning
- Image management and storage
- Supervised and unsupervised learning
- Training, validation and test data
- Common performance metrics
- Datasets of fungal images
- Classification of microscopy images
- Examples from research studies
- Machine learning on MALDI-TOF spectra
- Sequence classification tools
- Data quality concerns
- Bias and dataset representativeness
- External validation
- Regulatory frameworks for software
- Workflow integration
- Staff training and oversight
- Monitoring performance over time
AI and Digital Diagnostics in Mycology
By the end you will be able to:
- Explain how digital microscopy and whole-slide imaging work.
- Describe basic machine learning concepts and performance metrics.
- Critically assess published studies of AI-based fungal identification.
- Identify sources of bias and validation gaps in AI tools.
- Draft a responsible implementation plan for a digital diagnostic tool.
Where this programme sits in the school.
Where this programme can take you.
Roles and uses that build on what you learn here. Outcomes depend on your experience and local market.
- Laboratory scientist leading digital pathology projects
- Health data analyst in diagnostics
- Product specialist for digital microscopy companies
- Quality and validation officer for laboratory software
- Research assistant in AI for microbiology
Requirements, assessment and certificate.
Admission requirements
Laboratory or health data experience is recommended; no programming experience is required.
How you are assessed
Assessment combines participation in live sessions with exercises critically appraising tools and studies. The final framework is reviewed by the professor.
Final project
An evaluation framework for introducing a digital or AI tool into a mycology laboratory.
Certificate
Course Certificate
On completing the programme and its project, Fungaris issues a Course Certificate. Accreditations are assessed according to the training completed and the local regulations of each country.
How the programme grows, phase by phase.
- Digital microscopy and whole-slide imaging
- Machine learning fundamentals for laboratories
- Image-based identification of fungi
- AI for spectral and sequence data
- Validation, bias and regulation
- Implementing digital tools responsibly
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Questions about this programme
Do I need experience to study AI and Digital Diagnostics in Mycology?
Laboratory or health data experience is recommended; no programming experience is required.
How is AI and Digital Diagnostics in Mycology taught?
Live online, in 6 modules with a working professional, a small group and a final project.
How am I assessed?
Assessment combines participation in live sessions with exercises critically appraising tools and studies. The final framework is reviewed by the professor.
What is the final project?
An evaluation framework for introducing a digital or AI tool into a mycology laboratory.
What certificate do I get?
On completing the programme and its project, Fungaris issues a Course Certificate. Accreditations are assessed according to the training completed and the local regulations of each country.
What can I study after AI and Digital Diagnostics in Mycology?
The natural next step is Diploma in Medical Mycology Foundations.
Keep branching.
Join the next intake.
Pay in full with a 20% reduction, or reserve your place with 40% and pay the rest in monthly instalments. All prices are stated in US dollars (USD).