Automated eval vs human eval: which is better for catching subtle factual errors in medical text?

When you need to catch subtle factual errors in a specialized domain like medicine, **human evaluation** is the stronger choice. Here is why. Automated metrics (BLEU, ROUGE, exact match) check surface properties: does the output overlap with a reference, is it the right length, does it follow the expected format? None of these can tell you whether a drug interaction claim is medically accurate. The sentence 'take 500mg of ibuprofen every 2 hours' is grammatically perfect and might match a reference template, but a pharmacist knows the dosing interval is dangerously short. LLM-as-judge gets closer. A frontier model can flag obviously wrong claims and evaluate general coherence. But for subtle errors, like confusing two similar drug names or citing an outdated treatment protocol, a general-purpose model lacks the specialized training to catch every mistake. Human domain experts bring the knowledge that both automated tools lack. A physician or pharmacist reads the claim, checks it against their training and clinical experience, and identifies errors that look perfectly plausible to a non-expert. The cost is that human review is slow and expensive, so you cannot run it on every output. The standard pattern is to use automated checks and LLM judges for scale, then reserve human experts for the high-stakes, domain-specific verification layer.

For subtle factual errors in specialized text, **human evaluation** is the right tool because it brings domain expertise that automated metrics and general-purpose judges lack. Automated metrics check surface properties: word overlap, format compliance, length. They cannot verify whether a medical claim is correct. A dosage error or a confused drug name passes every surface check because it looks syntactically and structurally fine. LLM-as-judge can catch obvious errors and evaluate general quality at scale, but frontier models still make subtle mistakes in specialized domains. A judge might accept 'administer metformin for type 1 diabetes' because the sentence is coherent and confident, even though metformin is a type 2 treatment. Human domain experts catch what both miss. A physician reads the claim in clinical context and knows whether it is safe. The tradeoff is cost: human review is slow and cannot cover every output. The 2026 pattern is to layer all three. Automated checks gate formatting and structure. An LLM judge scores general quality and flags uncertain cases. Human experts review the flagged subset plus a stratified random sample. This gives you scale where automated tools are sufficient and depth where only a human will do.