HealthBench

Start with a narrow workflow that matches HealthBench's purpose: benchmark dataset and evaluation framework for measuring how language models perform on realistic health conversations. Use realistic but appropriately protected test data first. If the product is developer-facing, build a sandbox integration and define when the application must hand control to a clinician, staff member, or emergency pathway.

Compare AI output with the original source material. For documentation tools, verify medications, dosages, diagnoses, negations, dates, and follow-up instructions. For imaging or screening systems, inspect false positives and false negatives rather than only successful examples. For patient-facing assistants, test urgent symptoms, uncertainty, language variation, and accessibility needs.

Do not let a generated answer silently become the medical record or sole basis for a consequential clinical decision. Keep human review, audit trails, corrections, and escalation visible. Confirm that staff understand the product's intended use and that patients are not misled about whether they are interacting with an automated system.

Before production use, review consent, privacy, retention, security, data residency, integrations, regulatory obligations, and vendor validation evidence. Pricing is currently classified as free, but confirm current plans directly with the provider.