AI in life sciences and genomics: where it helps, and the safeguards it needs

Where AI already helps

• Protein structure. AI structure prediction changed structural biology; its developers shared the 2024 Nobel Prize in Chemistry.
• Sequence analysis. Machine learning supports variant interpretation and the analysis of genomic and transcriptomic data.
• Single-cell and imaging data. Models classify cell types, states and phenotypes across very large datasets.
• Literature and knowledge. Retrieval-based assistants help scientists search, connect and summarise research and protocols, with citations.
• Lab operations. Scheduling, inventory and documentation automation free scientists for science.

Safeguards that matter

• Privacy. Under the Australian Privacy Act, genetic information about an individual is sensitive information, with stricter rules for collection and use.
• Consent and ethics. Uses of data must stay within the consent and ethics approvals under which it was collected.
• Residency. Research data often has contractual and regulatory limits on where it can be processed.
• Reproducibility. Record data versions, model versions and parameters so results can be reproduced and audited.
• Validation. Treat model outputs as hypotheses to test, not findings.

Where to start

Begin with knowledge work, such as literature and protocol search over your own library, then move to analysis pipelines where data governance is already mature.