Getting Started with AI in Molecular and Genomic Pathology

A practical primer for the practising pathologist — what the technology actually does in molecular & genomic, what to ask of it, and how to start safely.

Molecular pathology has been running machine learning in production for years. Variant callers use statistical models. Copy-number analysis uses segmentation algorithms. Read alignment, base calling on nanopore platforms, and structural variant detection are all learned models. The bioinformatics pipeline in every molecular laboratory is, in substantial part, machine learning infrastructure that predates the current AI conversation.

What the primer covers

1. SectionThe subspecialty that has used machine learning longest without calling it that
2. SectionVariant interpretation: the real bottleneck
3. SectionCytogenetics and genomic profiling
4. SectionYour first 90 days
5. SectionValidation specifics
6. SectionFailure modes to anticipate
7. SectionWhat to watch next
8. SectionKey references

Who it is for

• Pathologists in molecular & genomic meeting diagnostic AI for the first time
• Laboratory leads assessing vendor claims and planning validation
• Trainees who want the regulatory and methodological context, not marketing