AI Literacy in Healthcare: Why Every Clinician Needs It

AI Literacy in Healthcare: Why Every Clinician Needs It

Introduction

Artificial intelligence (AI) is no longer experimental—it is entering daily healthcare practice. From diagnostic imaging to predictive models in the ICU, clinicians are increasingly confronted with AI-generated outputs. AI literacy refers to the knowledge and skills needed to understand, evaluate, and responsibly use these tools. This competency is now emphasized in policies such as the EU AI Act, which requires healthcare professionals to have sufficient AI literacy when using high-risk systems.

Defining AI Literacy in Healthcare

AI literacy is not about turning clinicians into programmers. Instead, it equips them to:

  • Understand the basic principles of how machine learning systems work.
  • Critically appraise validation studies and vendor claims.
  • Integrate AI into workflows without over-relying on its predictions.
  • Recognize limitations, biases, and errors in AI outputs.
AI Literacy AI Concepts & Methods Data Quality & Governance Bias & Fairness Validation & Appraisal Workflow Integration

Core competencies of AI literacy: methods, data governance, bias, validation, and workflow integration.

Why AI Literacy Matters

  • Patient safety. Misinterpreting AI outputs can lead to harm; literacy reduces risk.
  • Regulatory compliance. Under the EU AI Act, clinicians must exercise competent human oversight.
  • Professional responsibility. Understanding AI is now part of clinical competence, similar to pharmacology or imaging literacy.

Next in the Curriculum

With the foundation of AI literacy in place, the next stage of the curriculum explores the basics of data governance in clinical AI. If you haven’t yet, review the earlier posts: What is Medical AI? and What is Clinical AI?.