AI+ Medical Assistant

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.

Course outline

1. 1

   Lesson 1: Fundamentals of AI for Medical Assistants

   • Understanding AI and Its Healthcare Applications
   • The Role of AI in Medical Assistance
   • Case Studies
   • Hands-on Session: Functionality Survey and Stepwise Analysis of the Eka.care Patient-Side Application
2. 2

   Lesson 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
3. 3

   Lesson 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
4. 4

   Lesson 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
5. 5

   Lesson 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
6. 6

   Lesson 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
7. 7

   Lesson 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
8. 8

   Lesson 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