AI in Research: Why Human Review is Non-Negotiable

AI can generate a protocol in 12 seconds. It cannot replace the judgment of a researcher who has spent years understanding their experimental system. Here's how to think about the relationship.

What AI does well

Breadth. Claude has been trained on an enormous corpus of scientific literature. It knows the standard controls for flow cytometry, the common failure modes of ELISA, and the right statistical test for a three-group comparison with non-normal data.

Speed. Translating a research goal into a structured protocol takes hours when done manually. AI compresses this to seconds.

Consistency. AI doesn't forget to include a housekeeping control because it's 11pm. It applies the same rigor to every protocol.

Explainability. Ask the AI why it recommended a particular control or statistical test, and it will explain its reasoning in plain language.

What AI gets wrong

Institutional specifics. The AI doesn't know which instruments your lab has, what your cell lines actually behave like, or what your institution's biosafety committee requires. You do.

Reagent availability. Catalog numbers change. Suppliers discontinue products. Always verify that the specific reagents in an AI-generated protocol are actually available.

Field-specific intuition. An experienced researcher knows that their particular cancer cell line needs 48 hours of drug treatment to show an effect, not 24. The AI doesn't.

Regulatory context. The AI can note that IRB approval is required for human subjects research. It cannot tell you whether your specific protocol qualifies for exempt status at your institution.

How to work with AI-generated protocols

1. Treat the output as a first draft, not a finished product

2. Check every reagent against your institution's approved vendor list

3. Run it by a senior lab member before implementation

4. Run a pilot experiment with a small n before committing to a full study

5. Document deviations from the AI-generated protocol in your lab notebook

Olto Discovery is built around this philosophy. The AI disclosure appears on every protocol output. The scoring system gives you an honest assessment of the protocol's completeness. The refinement tools let you systematically improve it before you run it.

Science is still done by scientists. AI makes them faster.