AI+ Doctor™

AI+ Doctor Practitioner™ – Course Outline

Program Overview

The AI+ Doctor Practitioner™ program equips medical professionals with practical knowledge and applied skills to integrate Artificial Intelligence (AI) into healthcare delivery. The course focuses on clinical decision support, diagnostics, predictive analytics, patient management systems, ethical AI use in medicine, and emerging digital health technologies.

Target Audience

 • Medical Doctors (General Practitioners & Specialists)
 • Healthcare Consultants
 • Clinical Researchers
 • Hospital Administrators
 • Medical Technologists
 • Healthcare Innovation Officers

Course Duration

 • Recommended Duration: 40–60 Hours
 • Instructor-led training: 30–40 hours
 • Practical workshops & case studies: 10–20 hours

Learning Outcomes

 • Understand core AI concepts in healthcare
 • Use AI tools for diagnostics and clinical decision support
 • Interpret AI-generated medical insights
 • Apply predictive analytics in patient care
 • Understand medical imaging AI applications
 • Evaluate AI-based treatment recommendations
 • Ensure ethical, legal, and data privacy compliance
 • Integrate AI systems into hospital workflows

Assessment Methods

 • Quizzes after each module
 • Case study evaluations
 • Practical AI tool demonstrations
 • Final capstone project
 • Participation in simulations

Certification

 • AI+ Doctor Practitioner™ Certification
 • Validates applied AI knowledge in healthcare
 • Recognized for professional development in digital medicine

Tools & Technologies Covered (Optional)

 • AI diagnostic platforms (conceptual & demo-based)
 • Python-based healthcare analytics (intro level)
 • EHR simulation systems
 • AI imaging tools
 • Predictive analytics dashboards

Course Modules

Module 1: What is AI for Doctors?

 • AI in medical decision support and diagnostics
 • What makes AI in medicine unique
 • Types of machine learning in healthcare
 • Common algorithms in clinical applications
 • Real-world use cases across medical specialties
 • Myths and misconceptions about AI in healthcare
 • Current AI tools used by clinicians
 • Hands-on: Medical imaging analysis using AI tools

Module 2: AI in Diagnostics & Imaging

 • Neural networks in medical applications
 • Convolutional Neural Networks (CNNs) for imaging
 • Medical image modalities and multi-modal AI
 • Model training workflow in healthcare AI
 • Human-AI collaboration in diagnosis
 • FDA-approved AI diagnostic tools
 • Hands-on: AI-powered differential diagnosis

Module 3: Introduction to Fundamental Data Analysis

 • Clinical data types (EHRs, vitals, lab results)
 • Structured vs unstructured medical data
 • Data visualization in clinical decision-making
 • Pattern recognition in patient data
 • Identifying risk trends using AI
 • Interactive activity: AI insights from clinical notes

Module 4: Predictive Analytics & Clinical Decision Support

 • Predictive models for patient risk stratification
 • Logistic regression, decision trees, ensemble models
 • Real-time alert systems in hospitals
 • Sensitivity and specificity in clinical decisions
 • ICU and ER AI-assisted intervention systems

Module 5: NLP and Generative AI in Clinical Use

 • Natural Language Processing in healthcare
 • Large Language Models in medicine
 • Clinical prompt engineering
 • AI for documentation, summarization, and translation
 • Ambient AI in clinical workflows
 • Risks and limitations of generative AI
 • Case study: AI clinical documentation systems

Module 6: Ethical and Equitable AI Use

 • Algorithmic bias in healthcare AI
 • Explainability and transparency in AI models
 • Validation across diverse populations
 • Healthcare AI regulations and compliance (HIPAA, GDPR, FDA)
 • Ethical AI policies in clinical environments
 • Case study: Bias in medical detection systems

Module 7: Evaluating AI Tools in Practice

 • Evaluation metrics in healthcare AI
 • Confusion matrix and ROC curve interpretation
 • Matching AI metrics to clinical use cases
 • Critical review of AI outputs
 • Vendor evaluation and risk identification
 • AI procurement checklist for hospitals
 • Hands-on evaluation exercises

Module 8: Implementing AI in Clinical Settings

 • Identifying clinical AI use cases
 • Integrating AI into hospital workflows
 • Pilot planning and implementation strategies
 • Roles in AI deployment (clinical, technical, IT)
 • Monitoring AI errors and performance
 • Change management in healthcare AI adoption
 • Scaling AI across healthcare systems
 • Measuring AI impact post-deployment