Module 1: Fundamentals of AI for Medical Assistants
- 1.1 Understanding AI and Its Healthcare Applications
- 1.2 The Role of AI in Medical Assistance
- 1.3 Case Studies
- 1.4 Hands-on Session: Functionality Survey and Stepwise Analysis of the Eka.care Patient-Side Application
Module 2: Data Literacy for Medical Assistants
- 2.1 Healthcare Data Types and Management
- 2.2 Using Data Effectively in AI
- 2.3 Case Studies
- 2.4 Hands-On Session: Structured vs. Unstructured Data in Healthcare: A Practical Study Using Eka.Care Patient Health Record System
Module 3: AI in Patient Care Optimization
- 3.1 Enhancing Patient Interactions with AI
- 3.2 Predictive Analytics and Workflow Management
- 3.3 Case Studies
- 3.4 Hands-On Session: Eka.care in Action: Appointment Management, Smart Reminders & Tele-Consult Dashboards
Module 4: NLP and Generative AI in Medical Documentation
- 4.1 Foundations of NLP for Medical Assistants
- 4.2 Practical Applications and Risks
- 4.3 Case Studies
- 4.4 Hands-On Simulation Exercise
- 4.5 Hands-On Session: Automating Clinical Documentation Using Eka.care: Notes, Summaries, and Communication Workflows
Module 5: AI in Diagnostics and Screening
- 5.1 Diagnostic Support Tools
- 5.2 Real-World Applications and Simulation
- 5.3 Use Cases
- 5.4 Hands-On: AI-Powered Detection of Common Health Conditions: Review and Analysis of AI-Suggested Diagnostic Insights using Eka Care
Module 6: Ethics, Bias, and Regulation in AI for Healthcare
- 6.1 Recognizing and Addressing Bias in AI
- 6.2 Legal, Ethical, and Compliance Frameworks
- 6.3 Hands-On Exercise: Analyzing and Visualizing Bias in Artificial Intelligence Systems — Exploring Racial, Socioeconomic, and Demographic Disparities using Google’s What-If Tool
Module 7: Evaluating and Implementing AI Tools
- 7.1 Selecting and Planning for AI Adoption
- 7.2 Best Practices and Stakeholder Engagement
- 7.3 Case Study: Procurement and Early Deployment of AI Tools for Chest Diagnostics in a National Health Service Setting
- 7.4 Hands-On Simulation Exercise: Recognizing Red Flags in Vendor Solutions for AI in Medical Assistant
- 7.5 Hands-On Exercises: Evaluating the Relevance and Effectiveness of AI Models using the Zoho Analytics
Module 8: Cybersecurity and Emerging Trends in AI
- 8.1 Cybersecurity Risks and Protection
- 8.2 Future Trends and Preparing for Innovation
- 8.3 Case Studies: EY’s Strategic Transformation: Adapting to Emerging AI Technologies
- 8.4 Hands-On Exercises: Common Cybersecurity Threats in AI-Enabled Healthcare: A Hands-On Exploration Using Google Sheets
Tools you will explore
- TensorFlow
- Keras
- Python
- Natural Language Processing (NLP) Tools
- SQL
- Matplotlib
- Power BI
- Healthcare Data Integration Tools
- Electronic Health Record (EHR) Systems
- Patient Scheduling and Coordination Platforms
- AI-Powered Diagnostic Tools
- Medical Imaging Analysis Tools
