Review healthcare content and model behaviour with the context, caution and discipline high-impact systems require.
A useful evaluation does not ask a healthcare experts specialist for a vague opinion. It gives them a defined user, task, evidence set and rubric so their judgement can be compared, reviewed and reused. Payment, expected time, permitted materials and the review process should also be clear before a contributor accepts the work.
The required reviewer depends on what could go wrong. Routine clarity checks may need broad domain familiarity, while safety, professional standards or high-impact decisions need demonstrable healthcare experts experience and an explicit escalation path.
Reviewers identify the source, rule or observable outcome behind important scores. Written rationales make disagreement inspectable and help the project owner distinguish a genuine model failure from an underspecified instruction.
Accepted corrections should not disappear into a spreadsheet. The strongest examples become versioned cases that teams can rerun after prompt, model, tool or policy changes to see whether quality improved without introducing a new failure.
Specialists first review shared healthcare experts examples and compare material differences. Calibration exposes ambiguous instructions, missing evidence and inconsistent severity rules before those problems affect a larger evaluation or training dataset.
Projects should use approved, minimised evidence and clearly exclude confidential or unrelated material. Contributors need a visible way to pause, redact or escalate when a task would require information they are not authorised to disclose or assess.
SPECIALIST SKILLS
Explore all 8 capabilities. Each has its own qualification profile, example work, evidence contract and safety boundaries.
Evaluate clinical AI outputs for relevance, contraindications, uncertainty and appropriate escalation.
Explore skillReview patient-facing AI content for comprehension, tone, accessibility, appropriate limits and clear next-step guidance.
Explore skillTest whether AI preserves material facts, separates observation from inference and produces usable clinical documentation.
Explore skillTest whether clinical AI uses the presented evidence, considers material alternatives and recognises when urgent or specialist review is required.
Explore skillEvaluate medicine-related outputs for dose context, contraindications, interaction risk, source fidelity and appropriate referral to a pharmacist or prescriber.
Explore skillTest whether AI assigns and explains codes using the available clinical record, current conventions and clear evidence boundaries.
Explore skillEvaluate detection, prioritisation and reporting systems on authorised, de-identified imaging cases with subgroup and acquisition-condition analysis.
Explore skillTest synthesis of epidemiology, surveillance and intervention evidence while preserving uncertainty, population context and equity considerations.
Explore skillEXAMPLE PROJECTS
Every campaign defines its own scope, eligibility, quality checks and payment before work begins.
Every campaign defines its own scope, eligibility, quality checks and payment before work begins.
Every campaign defines its own scope, eligibility, quality checks and payment before work begins.