Recommended Prerequisites:

• Basic Medical Knowledge: Participants should have foundational knowledge of clinical practices, medical terminology and patient care processes.
• Familiarity with Healthcare Systems: A basic understanding of healthcare systems, including electronic health records (EHRs) and patient workflows will be beneficial.
• Interest in Technology Integration: A keen interest in exploring the intersection of AI and healthcare, along with a willingness to learn about AI applications in medical settings.
• Data Literacy: A basic understanding of data concepts, including data collection, analysis, and interpretation, is recommended for understanding AI models and metrics.
• Problem-Solving Mindset: Ability to approach challenges with a solutions-oriented mindset, especially when evaluating AI systems and adapting them to clinical settings.

Course Outline:

Lesson 1: What is AI for Doctors?

• From Decision Support to Diagnostic Intelligence
• What Makes AI in Medicine Unique?
• Types of Machine Learning in Medicine
• Common Algorithms and What They Do in Healthcare
• Real-World Use Cases Across Medical Specialties
• Debunking Myths About AI in Healthcare
• Real Tools in Use by Clinicians Today
• Hands-on: Medical Imaging Analysis using MediScan AI

Lesson 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
• 2.7 Hands-on Activity: Exploring AI-Powered Differential Diagnosis with Symptoma

Lesson 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

Lesson 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

Lesson 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

Lesson 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

Lesson 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”
• 7.8 Hands-on

Lesson 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

All necessary course materials are included.