A practical framework for deciding where AI adds value in research and for validating, documenting, and integrating models into reproducible workflows while managing risk.
Welcome — use AI without losing your science. Many research teams want the speed and insight AI can provide, but worry about opaque models, unreproducible pipelines, and accidental harm. This resource helps you decide where AI genuinely adds value for your research, validate models so results can be trusted and...
Deciding where AI adds value in your research. Not every research problem needs machine learning. Use this decision guide to find opportunities where AI gives a clear advantage and where risks remain manageable. Quick decision criteria. Repetition and scale: Are you repeating the same labeling, measurement, or QC task...
How to write a practical Model Card for research. A Model Card is a short, structured summary that explains what a model does, who should use it, and where it fails. For research, a model card is valuable for reviewers, collaborators, and later users who must judge whether a model is fit for their context. Essential...
Integrating models into reproducible research workflows. Packaging a validated model so others can reproduce and reuse it requires more than good metrics. This guide lists practical steps for research teams to integrate models into reproducible pipelines. Core practices. Environment capture: Use containers (e.g...
Two short case studies: low-risk win and high-risk caution. Case A — Lab imaging QC (low risk). A microscopy core built a small CNN to flag out-of-focus images so technicians could re-acquire them. They used an internal labeled set (5,000 images), compared a simple threshold baseline, and deployed the model as a...