Course Prerequisites:
Basic Medical Terminology: Familiarity with healthcare concepts and terminology.
Foundational Knowledge in AI: Understanding of machine learning and algorithms.
Data Analytics Skills: Ability to analyze and interpret medical data.
Programming Skills: Proficiency in Python or similar languages for AI tools.
Understanding of Healthcare Systems: Knowledge of clinical workflows and medical practices.
Modules:
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
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
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
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,
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
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
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
7.4 Hands-On Simulation Exercise: Recognizing Red Flags in Vendor Solutions for AI in
7.5 Hands-On Session: Evaluating the Relevance and Effectiveness of AI Models Using
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