AI+ Medical Assistant™

Revolutionize Healthcare Support with AI-Powered Medical Assistance
  • 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.

Módulos

  • Module 1: Fundamentals of AI for Medical Assistants:
    1. 1.1 Understanding AI and Its Healthcare Applications
    2. 1.2 The Role of AI in Medical Assistance
    3. 1.3 Case Studies
    4. 1.4 Hands-on Session: Functionality Survey and Stepwise Analysis of the Eka.care Patient-Side Application
  • Module 2: Data Literacy for Medical Assistants:
    1. 2.1 Healthcare Data Types and Management
    2. 2.2 Using Data Effectively in AI
    3. 2.3 Case Studies
    4. 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:
    1. 3.1 Enhancing Patient Interactions with AI
    2. 3.2 Predictive Analytics and Workflow Management
    3. 3.3 Case Studies
    4. 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:
    1. 4.1 Foundations of NLP for Medical Assistants
    2. 4.2 Practical Applications and Risks
    3. 4.3 Case Studies
    4. 4.4 Hands-On Simulation Exercise
    5. 4.5 Hands-On Session: Automating Clinical Documentation Using Eka.care: Notes, Summaries, and Communication Workflows
  • Module 5: AI in Diagnostics and Screening:
    1. 5.1 Diagnostic Support Tools
    2. 5.2 Real-World Applications and Simulation
    3. 5.3 Use Cases
    4. 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:
    1. 6.1 Recognizing and Addressing Bias in AI
    2. 6.2 Legal, Ethical, and Compliance Frameworks
    3. 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:
    1. 7.1 Selecting and Planning for AI Adoption
    2. 7.2 Best Practices and Stakeholder Engagement
    3. 7.3 Case Study: Procurement and Early Deployment of AI Tools for Chest Diagnostics in a National Health Service Setting
    4. 7.4 Hands-On Simulation Exercise: Recognizing Red Flags in Vendor Solutions for AI in Medical Assistant
    5. 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:
    1. 8.1 Cybersecurity Risks and Protection
    2. 8.2 Future Trends and Preparing for Innovation
    3. 8.3 Case Studies: EY’s Strategic Transformation: Adapting to Emerging AI Technologies
    4. 8.4 Hands-On Exercises: Common Cybersecurity Threats in AI-Enabled Healthcare: A Hands-On Exploration Using Google Sheets

Herramientas de IA

  • 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
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