AI+ Medical Assistant Practitioner™

Štruktúra kurzu

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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

Optional Module: AI+ Medical Assistant Practitioner

• 1.1 What Are AI Agents?
• 1.2 How Does an AI Agent Work in Medical Assistance?
• 1.3 Core Characteristics of AI Agents
• 1.4 Importance of AI Agents in Healthcare
• 1.5 Significance for Patient Experience & Clinical Outcomes
• 1.6 Types of AI Agents
• 1.7 Applications and Trends
• 1.8 Case Study — AI Clinical Documentation at Mayo Clinic