Artificial Intelligence
Artificial Intelligence in Pathology: Foundations
Dr Lina Berg · 14 hours · Beginner · ★ 4.8 (2,140 learners)
Learning outcomes
- Explain core machine learning concepts in a diagnostic context
- Describe how models are trained and validated on pathology data
- Identify appropriate and inappropriate uses of AI in reporting
- Interpret published AI performance metrics critically
Curriculum
Module 1: What AI is and is not
Recorded lectures, reading list, worked examples and a short self-assessment.
Module 2: Data in pathology: slides, labels and metadata
Recorded lectures, reading list, worked examples and a short self-assessment.
Module 3: Supervised learning fundamentals
Recorded lectures, reading list, worked examples and a short self-assessment.
Module 4: Model evaluation metrics
Recorded lectures, reading list, worked examples and a short self-assessment.
Module 5: Reading an AI validation paper
Recorded lectures, reading list, worked examples and a short self-assessment.
Module 6: Regulatory context
Recorded lectures, reading list, worked examples and a short self-assessment.
Module 7: Case discussions
Recorded lectures, reading list, worked examples and a short self-assessment.
Certification
Certificate of completion. Awarded on completion of all modules.
Course certificates state clearly whether they represent participation, completion, assessment or CME credit. They do not constitute professional certification or a licence to practise.