AI for Scientists and Lab Work: Faster Analysis, Careful Claims

Science is slow, meticulous and increasingly data-heavy. AI can help at many stages, but reproducibility and honesty are non-negotiable.

• Literature review: finding and summarising papers (always read the key ones).
• Experimental design: brainstorming controls, confounders and sample sizes.
• Data analysis: code help, statistics explanations and visualisation.
• Image analysis: counting cells, segmenting structures and classifying microscopy images.
• Lab automation: scheduling and robotic workflows.
• Modelling: speeding up simulations and predictions.
• Writing: methods drafts, abstracts and grant applications.
• Lab notebooks: transcribing and organising notes.

• Fabricated citations and results in AI-written text.
• Black-box models that fit data without explaining it.
• Data leakage and overfitting.
• Bias in training data.
• Reproducibility: results that can't be rerun or checked.
• Confidentiality: unpublished data and peer-review material shouldn't be pasted into public tools.
• Integrity: following journal and funder rules on disclosure and authorship.

1. Pre-register or document analysis plans.
2. Validate models on independent data.
3. Share code and data where possible.
4. Check every citation and quotation.
5. Disclose AI use as required.
6. Keep human responsibility for conclusions.

AI can speed up the routine and reveal patterns. Rigour, transparency and honest reporting keep science trustworthy.