Evidence awareness
Separate plausible language from verified facts, and prefer current primary sources for consequential claims.
Clinical AI Literacy
Learn how large language models work, where they fail and when a qualified person needs to review their output.
Practical AI literacy
Five questions before an AI answer influences care
Fluency is not evidence. Use this short check whenever an AI output may affect a diagnosis, treatment, referral, triage decision, or patient understanding.
- What is this for?Confirm intended use and whether this decision is too consequential for the tool.
- What information is missing?Look for history, examination, context, timing, patient preferences, and local practice constraints.
- Can I verify it?Check important claims against current primary guidance, the medical record, or an appropriate professional.
- Who could be harmed?Consider false reassurance, delay, bias, privacy loss, automation bias, and effects on underserved groups.
- When do I stop or escalate?Define red flags, uncertainty thresholds, and the human decision maker before use.
For patients and caregivers
AI can help organize questions and explain general concepts. It should not replace urgent evaluation, individualized diagnosis, or treatment from a qualified clinician. Do not enter identifiable health information unless you understand how it will be used and protected.
Core skills
A shared language for safer use
Uncertainty
Ask what the system does not know, what assumptions it made, and what evidence could change the answer.
Bias and equity
Check whether training data, validation, access, or workflow differences could produce uneven benefit or harm.
Privacy and security
Use approved tools and minimum necessary data; understand retention, access, vendor use, and breach response.
Human factors
Design for automation bias, alert fatigue, time pressure, handoffs, and the real conditions of clinical work.
Incident learning
Make near miss reporting easy, protect reporters, investigate system causes, and share corrective actions.
get started
Ready to implement AI governance?
Build practical AI literacy around real clinical workflows, measurable risks, and the judgment to question an AI output before it affects patient care.
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