Est-ce que RestorFX dispose d'une garantie ?

Oui, RestorFX est un traitement permanent. Nous offrons une garantie de tenue dans le temps de 10 ans lorsque le traitement est appliqué sur une peinture d'origine (n'ayant pas été repeinte ou reçu de traitements préalables). Cette garantie couvre le fait que le traitement ne s'écaille pas et ne délamine pas.

Combien de temps dure le traitement RestorFX ?

RestorFX est un traitement permanent. Le véhicule est généralement immobilisé 24 à 48 heures pour le traitement complet.

Quelle est la différence entre polissage/lustrage et RestorFX ?

Le polissage retire une couche de vernis (abrasion). RestorFX est un procédé technique et chimique en 13 étapes qui fusionne avec le vernis pour le réparer durablement, sans devoir repolir régulièrement.

Est-ce moins bien qu'une nouvelle peinture ?

Ni mieux ni moins bien : cela dépend du dommage. RestorFX conserve la peinture d'origine, évite les écarts de teinte, et c'est plus rapide et souvent moins coûteux. La peinture s'impose en cas de dégâts majeurs.

Différence entre RestorFX et traitement céramique ?

RestorFX corrige les défauts (rayures, oxydation). La céramique protège et facilite l'entretien. Les deux sont compatibles et complémentaires.

Entretien après le traitement RestorFX ?

Évitez toute action chimique ou mécanique pendant 2–3 semaines (pas de rouleaux, pas d'essuyage). Un simple rinçage au jet est recommandé durant cette période.

OneClick.ai | Clinical AI Matrix

Clinical AI Matrix

Curated workforce readiness pathways for GenAI-integrated trial operations.

Low riskMCI-01

Introduction to AI in Clinical Research

All roles

All phases

○

Low riskMCI-02

Human Intelligence vs Artificial Intelligence

All roles

All phases

Share○

Low riskMCI-03

Fundamentals of Artificial Intelligence

All roles

All phases

○

Medium riskMCI-04

AI in Clinical Trials

Study managers

All phases

Share○

Medium riskMCI-05

AI Literacy Framework

All roles

All phases

Share○

Medium riskMCI-06

AI Literacy Practices

All roles

All phases

○

High riskMCI-07

Evaluating AI

Risk-based monitoring

Conduct

Share○

Medium riskMCI-08

Using Copilot in Clinical Operations

Clinical trial managers

Conduct

Share○

High riskMCI-09

Clinical Case Studies Using Copilot

Clinical trial managers

Conduct

Share○

High riskMCI-10

AI Governance in Pharma

Clinical operations directors

All phases

Share○

High riskMCI-11

AI Governance aligned with GxP

Clinical operations directors

All phases

Share○

Medium riskMCI-12

Implementing AI in Clinical Organizations

Trial delivery leads

Startup

Share○

Low riskMCI-13

Future of AI in Clinical Trials

Trial delivery leads

All phases

○

Low riskMCI-14

Understand what AI is before trusting what it says (Foundations)

All roles

All phases

○

Low riskMCI-15

Understand what AI is before trusting what it says (The Machine)

All roles

All phases

○

OneClick.ai • AI Literacy • Interactive Timeline

Understand what AI is before trusting what it says.

A practical training module to help learners distinguish core AI categories, understand where language models fit, and apply safer judgment in clinical and operational contexts.

Foundation Topic

The AI hierarchy

Explore the animated circles and click each level to understand how AI, ML, DL, and LLMs relate to one another.

Open topic

AI Layer

Artificial Intelligence

The broad umbrella layer that includes intelligent systems used for decision support, orchestration, optimization, prediction, generation, and assistance.

Open topic

Layer 2

Machine Learning

A subset of AI that learns from examples to score, classify, forecast, or predict outcomes.

Open topic

Layer 3

Deep Learning

A specialized form of ML using layered neural networks for images, speech, and other complex signal data.

Open topic

Layer 4

Large Language Models

Deep Learning systems specialized for language tasks such as summarization, rewriting, extraction, and generation.

Open topic

Final concept

AI agents

Understand how agents differ from a model alone by combining reasoning, tools, memory, and action across multi-step workflows.

Open topic

Scenario Practice

Classification quiz deck

A larger practice set with 25 questions to reinforce classification across AI, ML, DL, LLM, Agent, and non-AI examples.

Open topic

AI literacy is not about hype. It is about safe judgment.

Teams need a usable framework: understand the hierarchy, know what kind of system they are dealing with, identify the likely risks, and keep humans accountable for important decisions.

A standalone interactive module explaining how a language system handles prompts, builds context, and produces output in clinical-trial examples.

Animated circles

LLM

Core idea

AI refers to systems that support intelligent behavior such as decision support, reasoning, optimization, or generation.

Visual explanation

Use this space for a diagram or visual showing planning, routing, prioritization, or support logic.

How to recognize the AI layer

Choose the AI category when the scenario clearly describes intelligent assistance or decision support, but does not yet give enough detail to classify it more specifically as ML, DL, or LLM.

Broad orchestration across systems often signals the AI umbrella.

Optimization, routing, and support can belong here.

Use a narrower category only when the mechanism is clearly predictive, vision-based, or language-based.

Why this matters

“AI” is often accurate, but it is frequently too broad to guide safe usage on its own.

Broad labels can hide how a system actually works.
Precise naming supports better expectations and controls.
Training should help learners move beyond generic AI language.

Core idea

Machine Learning learns statistical patterns from data instead of relying only on fixed instructions.

Visual explanation

Use this space for a score chart, trend visual, or structured-data prediction illustration.

⚠

Why this matters

Predictions can feel authoritative, but they remain dependent on training data and context.

Predictions are not certainties.
Bias, drift, or poor data can degrade reliability.
Human review is still necessary for important decisions.

Core idea

Deep Learning uses multiple neural network layers to detect progressively more abstract patterns in complex inputs.

Visual explanation

Use this space for an imaging, signal, or neural-network themed visual explanation.

◉

Why this matters

Deep learning can look very impressive, but users should not confuse strong visuals with guaranteed correctness.

Visually convincing output can still be wrong.
False positives and false negatives remain possible.
Interpretability is often more limited than users expect.

Core idea

LLMs are language-focused models optimized for reading, generating, summarizing, and transforming natural language.

Visual explanation

Use this space for a prompt-response, note-summary, or language workflow visual.

✎

Why this matters

Fluent output can create false trust, especially when the wording feels polished and confident.

Generated language can omit, distort, or invent details.
Users may overtrust answers because they sound coherent.
Verification and human oversight remain essential.

⚙

How the machine works

Click each machine block to see what happens to a clinical-trial prompt at that stage.

Interactive machine

InputPrompt → tokens

ProcessContext → attention

OUT

OutputProbabilities → text

Stage 1

Input: prompt becomes machine-readable units

The model does not read a clinical-trial sentence as meaning first. It first breaks the text into smaller language units that can be mapped into numbers.

🧠

Why this matters

Understanding the three-stage machine helps learners see why outputs are statistical, context-sensitive, and still require human review.

Core idea

Agents combine a model with tools, memory, and decision steps so they can execute multi-step workflows instead of only producing one reply.

How to recognize an agent

It can plan several steps toward an objective.

It can use tools such as search, APIs, or enterprise systems.

It can keep state or memory across steps.

It can take or recommend actions, not just generate text.

⚠

Why this matters

Agents increase capability, but they also increase risk because they can trigger actions across systems and workflows.

Interactive deck

Select the best answer below. Once you answer, the feedback appears and the reveal button becomes available.

🧠 Question 1 of 25

What is the best category for this use case?

📌

Scenario

Deck progress

25 questions

💡

Practice tip

✓

Explanation

Review the correct category, why it fits, and the main risk to remember.

✅ Review

Why

Risk to remember

👤

Learner answer

Not selected yet

Clinical AI Matrix

Introduction to AI in Clinical Research

Human Intelligence vs Artificial Intelligence

Fundamentals of Artificial Intelligence

AI in Clinical Trials

AI Literacy Framework

AI Literacy Practices

Evaluating AI

Using Copilot in Clinical Operations

Clinical Case Studies Using Copilot

AI Governance in Pharma

AI Governance aligned with GxP

Implementing AI in Clinical Organizations

Future of AI in Clinical Trials

Understand what AI is before trusting what it says (Foundations)

Understand what AI is before trusting what it says (The Machine)

The AI hierarchy

Artificial Intelligence

Machine Learning

Deep Learning

Large Language Models

AI agents

Classification quiz deck

AI literacy is not about hype. It is about safe judgment.

Use the right label

Verify before acting

Train for discernment