Generative AI
creates new content based on existing materials in its training data, powering tools like chatbots, coding assistants and image generators.
In careTurns a discharge letter into plain language a patient actually reads.
Lyon 3 · AI & Marketing Research Lab
A two-minute scroll
Medical AI
Literacy.
What it is, what it cannot do, and the part you play in care.
Scroll
AI is already woven into health. Sometimes you can see it. Sometimes it works invisibly — and still shapes real outcomes.
Adapted from OECD/European Union (2026), p. 5.
The basics
Three moves.
Every single time.
It takes in dataVitals, notes, scans, records — whatever you feed it.
1
It infers patternsStatistics, not understanding. It computes what is most probable.
2
It produces an outputA prediction, a draft, a recommendation — or a decision.
3
The legal definition
A machine-based system that … infers, from the input it receives, how to generate outputs such as predictions, content, recommendations or decisions that can influence physical or virtual environments.
Definition of AI in the EU AI Act (European Parliament, 2024), quoted in OECD/European Union (2026), p. 6.
Two families
One creates.
One predicts.
Predictive AI
is trained to detect patterns in existing data and to forecast what is likely to happen next.
In careFlags the patient on the ward most likely to deteriorate tonight.
Both definitions: OECD/European Union (2026), p. 6, “Types of AI”. Care examples written for this page.
Keep this in mind
It can sound caring.
It can sound certain.
It is not human.
“AI systems use data to produce outputs that are statistically likely to meet an objective or reward. These systems do so without awareness, understanding or intent.”
0%
of surveyed US teens had used AI “companions” — tools designed to hold meaningful conversations.
OECD/European Union (2026), p. 7, citing Robb & Mann (2025).
A simple framework
Four moves.
One AI-literate you.
Engage, create, manage, shape. Each one builds on the last.
Domains of the AILit Framework: OECD/European Union (2026), p. 8, Figure 1.
Move 1 of 4
Engage
“Become a critical and responsible participant in a world marked by AI”
Spot it. Question it. Decide.
Competence: “Evaluate whether AI outputs should be accepted, revised or rejected.”
Move 2 of 4
Create
“Use AI as a creative partner while maintaining human agency”
Let it draft. You keep the voice.
Competence: “Direct generative AI systems to elicit feedback, refine results and support reflection.”
Move 3 of 4
Manage
“Divide work intentionally between humans and AI”
Hand off the routine. Keep the judgement.
Competence: “Decide whether to use AI systems based on the nature of the task.”
Move 4 of 4
Shape
“Improve AI systems to reflect human values”
Report what fails. Ask who it was built for.
Competence: “Investigate how an AI system is intended to work, whom it is designed for and what its limitations are.”
Before you trust it
Three checks.
Every tool.
Fairness, transparency, data. Keep scrolling — one at a time.
Check 1 of 3
Who is it fair to?
If a group is thin in the training data, the model serves them worse — and nobody is told.
Bias inherently exists in AI systems, which can also reflect societal biases embedded in training data or algorithm design.
OECD/European Union (2026), p. 20, Knowledge 2.5.
Check 2 of 3
Can you see how it works?
A tool you cannot question is a tool you cannot trust. Ask what it does, who it was built for, where it fails.
Ensuring transparency, explainability, and intelligibility.
WHO principle, in PAHO/IDB (2024), p. 38.
Check 3 of 3
Where does the data go?
Health information is the most personal data there is. Know what is collected, who sees it, how it is protected.
Privacy, confidentiality, and security of data use must be foundational to every AI development.
PAHO/IDB (2024), p. 37.
Stay curious.
Stay in control.
“Embracing AI in public health is a collective effort to ensure no one is left behind.”
Dr. Jarbas Barbosa, Director, PAHO/WHO, in PAHO/IDB (2024).
Return to the surveyIllustrations and quoted excerpts are reproduced or adapted from OECD/European Union (2026), Empowering learners for the age of AI: An AI literacy framework for primary and secondary education, OECD Publishing, Paris, https://doi.org/10.1787/65cd27d4-en, licensed CC BY 4.0. This is an adaptation of an original work by the OECD and the European Union. The opinions expressed and arguments employed in this adaptation should not be reported as representing the official views of the OECD, its Member countries or the European Union. Illustrations by Abiyasa Adiguna. Health excerpts: PAHO/IDB (2024), Artificial intelligence in public health: Readiness assessment toolkit, Washington, D.C. Research stimulus for educational purposes; not medical advice.