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AI+ Medical Assistant Practitioner™
- Patient Interaction Excellence: Learn how AI enhances patient communication, appointment scheduling, and follow-up care to improve the patient experience.
- Clinical Workflow Efficiency: Master AI tools for streamlining patient intake, medical record management, and lab result analysis to optimize clinical operations.
- Data-Driven Decision Support: Gain expertise in using AI to assist healthcare providers with accurate diagnostics, treatment suggestions, and patient monitoring.
- Enhanced Medical Administration: Prepare to support healthcare teams with AI-driven administrative tasks, reducing errors, improving accuracy, and enabling faster decision-making.
Certificate Code
AP 5010
Exam Format
AI-Driven Remote Exam Proctoring
Course Overview
Important details and certification information
Instructor-led OR Self-paced course + Official exam + Digital badge
Instructor-Led: 3 Days (live or virtual)
A basic understanding of medical terminology, foundational AI and machine-learning concepts, data analytics skills for interpreting medical data, proficiency in programming languages like Python, and knowledge of healthcare systems and clinical workflows are essential for this course.
50 questions, 70% passing, 90 minutes, online proctored exam
Certification Modules
- 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
- 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
- 3.1 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
- 4.1 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
- 5.1 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
- 6.1 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
- 7.1 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
- 8.1 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
- 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