Courses AI certifications (AI CERTs®)

AI+ Doctor Practitioner™

  • Price 109 EUR + VAT
  • E-learning Start any time
  • Duration 8 hours of content, self-paced
  • Language English
  • Topic AI Healthcare

How you can take it

  • E-learning Start any time · 109 EUR + VAT Order
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Overview

What the course covers

The course, the materials and the exam are in English. The description below is the original AI CERTs® text.

  • Clinical Intelligence Focus: Designed for medical professionals to integrate AI into patient care and diagnostics
  • Data-Driven Decisions: Equips doctors with tools to interpret AI-generated insights for precise treatment planning
  • Comprehensive Medical AI Knowledge: Covers AI applications from predictive analytics to medical imaging and virtual health
  • Future-Ready Expertise: Empowers healthcare practitioners to lead AI-driven innovations in clinical practice

Prerequisites

Basic medical knowledge, Familiarity with healthcare systems, Interest in technology integration, Data literacy, Problem-Solving mindset

Audience

Who is it for

Medical Practitioners: Enhance patient care with AI-driven tools for diagnostics, treatment planning, and clinical decision support.

Medical Students: Build future-ready skills by learning how AI is transforming modern medicine and clinical workflows.

Healthcare Administrators: Leverage AI to improve hospital operations, resource management, and patient service delivery.

Clinical Researchers: Apply AI for advanced data analysis, predictive modeling, and evidence-based medical research.

Health Tech Enthusiasts: Explore the synergy between AI and healthcare to innovate and contribute to next-gen medical solutions.

In the price

What the price includes

  • Self-paced online course with the full learning material
  • The official AI CERTs® exam
  • Digital badge after passing the exam

Course content

Syllabus

Module 1: What is AI for Doctors?
  • 1.1 From Decision Support to Diagnostic Intelligence
  • 1.2 What Makes AI in Medicine Unique?
  • 1.3 Types of Machine Learning in Medicine
  • 1.4 Common Algorithms and What They Do in Healthcare
  • 1.5 Real-World Use Cases Across Medical Specialties
  • 1.6 Debunking Myths About AI in Healthcare
  • 1.7 Real Tools in Use by Clinicians Today
Module 2: AI in Diagnostics & Imaging
  • 2.1 Introduction to Neural Networks: Unlocking the Power of AI
  • 2.2 Convolutional Neural Networks (CNNs) for Visual Data: Seeing with AI’s Eyes
  • 2.3 Image Modalities in Medical AI: AI’s Multi-Modal Vision
  • 2.4 Model Training Workflow: From Data Labeling to Deployment – The AI Lifecycle in Medicine
  • 2.5 Human-AI Collaboration in Diagnosis: The Power of Augmented Intelligence
  • 2.6 FDA-Approved AI Tools in Diagnostic Imaging: Trust and Validation
Module 3: Introduction to Fundamental Data Analysis
  • 3.1 Understanding Clinical Data Types – EHRs, Vitals, Lab Results
  • 3.2 Structured vs. Unstructured Data in Medicine
  • 3.3 Role of Dashboards and Visualization in Clinical Decisions
  • 3.4 Pattern Recognition and Signal Detection in Patient Data
  • 3.5 Identifying At-Risk Patients via Trends and AI Scores
  • 3.6 Interactive Activity: AI Assistant for Clinical Note Insights
Module 4: Predictive Analytics & Clinical Decision Support – Empowering Proactive Patient Care
  • 4.1 Predictive Models for Risk Stratification – Sepsis and Hospital Readmissions
  • 4.2 Logistic Regression, Decision Trees, Ensemble Models
  • 4.3 Real-Time Alerts – Early Warning Systems (MEWS, NEWS)
  • 4.4 Sensitivity vs. Specificity – Metric Choice by Clinical Need
  • 4.5 ICU and ER Use Cases for AI-Triggered Interventions
Module 5: NLP and Generative AI in Clinical Use
  • 5.1 Foundations of NLP in Healthcare
  • 5.2 Large Language Models (LLMs) in Medicine
  • 5.3 Prompt Engineering in Clinical Contexts
  • 5.4 Generative AI Use Cases – Summarization, Counselling Scripts, Translation
  • 5.5 Ambient Intelligence: Next-Gen Clinical Documentation
  • 5.6 Limitations & Risks of NLP and Generative AI in Medicine
  • 5.7 Case Study: Transforming Clinical Documentation and Enhancing Patient Care with Nabla Copilot
Module 6: Ethical and Equitable AI Use
  • 6.1 Algorithmic Bias – Race, Gender, Socioeconomic Impact
  • 6.2 Explainability and Transparency (SHAP and LIME)
  • 6.3 Validating AI Across Populations
  • 6.4 Regulatory Standards – HIPAA, GDPR, FDA/EMA Compliance
  • 6.5 Drafting Ethical AI Use Policies
  • 6.6 Case Study – Biased Pulse Oximetry Detection
Module 7: Evaluating AI Tools in Practice
  • 7.1 Core Metrics: Understanding the Basics
  • 7.2 Confusion Matrix & ROC Curve Interpretation
  • 7.3 Metric Matching by Clinical Context
  • 7.4 Interpreting AI Outputs: Enhancing Clinical Decision-Making
  • 7.5 Critical Evaluation of Vendor Claims: Ensuring Reliability and Effectiveness
  • 7.6 Red Flags in Commercial AI Tools: Recognizing and Mitigating Risks
  • 7.7 Checklist: “10 Questions to Ask Before Buying AI Tools”
Module 8: Implementing AI in Clinical Settings
  • 8.1 Identifying Department-Specific AI Use Cases
  • 8.2 Mapping AI to Workflows (Pre-diagnosis, Treatment, Follow-up)
  • 8.3 Pilot Planning: Timeline, Data, Feedback Cycles
  • 8.4 Team Roles – Clinical Champion, AI Specialist, IT Admin
  • 8.5 Monitoring AI Errors – Root Cause Analysis
  • 8.6 Change Management in Clinical Teams
  • 8.7 Example: ER Workflow with Triage AI Integration
  • 8.8 Scaling AI Solutions Across the Healthcare System
  • 8.9 Evaluating AI Impact and Performance Post-Deployment
Optional Module: AI+ Doctor Practitioner Agents
  • 1.1 What Are AI Agents?
  • 1.2 How Does an AI Agent Work in Healthcare?
  • 1.3 Core Characteristics of AI Agents
  • 1.4 Importance of AI Agents (General + Healthcare)
  • 1.5 Significance of AI Agents in Healthcare
  • 1.6 Types of AI Agents
  • 1.7 Applications and Trends in Healthcare
  • 1.8 Case Study – AI Agents for Healthcare Data Unification

Exam

About the exam

  • 50 Questions
  • 90 min Time limit
  • 70% Pass mark
  • Online proctored exam

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