AI Innovations in Healthcare: From Detection to Treatment

Course Info

Date: Jan-27-2025

Length: 1 Week

City: Kuala Lumpur

Fees: 3,980

Type: In Classroom

Available Dates

  • Jan-27-2025

    Kuala Lumpur

  • Apr-28-2025

    Kuala Lumpur

  • July-28-2025

    Kuala Lumpur

  • Oct-27-2025

    Kuala Lumpur

Dates in Other Venues

  • Dec-30-2024

    Istanbul

  • Dec-30-2024

    Paris

  • Dec-30-2024

    Singapore

  • Jan-27-2025

    Amsterdam

  • Jan-27-2025

    Barcelona

  • Jan-27-2025

    Paris

  • Jan-27-2025

    Istanbul

  • Jan-27-2025

    Singapore

  • Jan-27-2025

    London

  • Feb-24-2025

    Dubai

  • Mar-24-2025

    London

  • Apr-28-2025

    Dubai

  • Apr-28-2025

    Paris

  • Apr-28-2025

    Istanbul

  • Apr-28-2025

    Singapore

  • Apr-28-2025

    Barcelona

  • Apr-28-2025

    Amsterdam

  • May-26-2025

    London

  • June-23-2025

    Dubai

  • July-28-2025

    Paris

  • July-28-2025

    Barcelona

  • July-28-2025

    Amsterdam

  • July-28-2025

    Istanbul

  • July-28-2025

    London

  • July-28-2025

    Singapore

  • Aug-25-2025

    Dubai

  • Sep-22-2025

    London

  • Oct-27-2025

    Dubai

  • Oct-27-2025

    Barcelona

  • Oct-27-2025

    Amsterdam

  • Oct-27-2025

    Paris

  • Oct-27-2025

    Singapore

  • Oct-27-2025

    Istanbul

  • Nov-24-2025

    London

  • Dec-22-2025

    Dubai

Course Details

Course Outline

5 days course

Introduction to AI in Healthcare


  • Overview of AI in Healthcare: Impact and evolution of AI technologies in the medical field.
  • Key Areas of Application: Exploration of how AI is used in diagnostics, patient care, operations, and public health.
  • AI Tools & Technologies: Exploring essential tools & technologies behind AI in healthcare.
  • Challenges and Opportunities: Discussion on data privacy, ethical considerations, and the potential of AI to transform healthcare.
  • Case Studies: Real-world examples of AI positively impacting healthcare outcomes.

Python Basics & Data Exploration


  • Python for Healthcare: Introduction to using Python for data analysis, focusing on healthcare-specific libraries and tools.
  • Exploratory Data Analysis (EDA): Techniques to visualize and analyze health datasets to uncover patterns and insights.
  • Hands-On Workshop: Participants conduct EDA on provided healthcare datasets, learning to interpret medical data effectively.
  • Data Management Best Practices: Ensuring data quality, security, and compliance with health data regulations.
  • Discussion: Overcoming challenges in healthcare data analysis.

Machine Learning in Healthcare


  • Machine Learning Fundamentals: Overview of machine learning techniques applicable to healthcare.
  • Predictive Models for Disease Detection: Developing models to predict and diagnose diseases early using patient data.
  • Workshop: Building and training predictive models using real patient datasets.
  • Model Evaluation and Validation: Techniques to assess model performance and ensure reliability in clinical settings.
  • Interactive Q&A: Addressing common questions and challenges in implementing ML in healthcare.

Deep Learning & Advanced AI Applications


  • Introduction to Deep Learning: Utilizing deep neural networks for complex healthcare applications, such as medical imaging and genetic data analysis.
  • Deep Learning for Skin Disease Detection: Understanding how to implement CNN models for Disease Detection.
  • Model Building and Training: Step-by-step instructions on building a CNN model using TensorFlow/Keras
  • Advanced AI Technologies: Exploring innovative applications such as AI in robotic surgery and patient management systems.

Implementing & Managing AI Solutions


  • Deployment Strategies: Best practices for deploying AI solutions in healthcare settings.
  • Ethical AI Use: Comprehensive discussion on managing ethical issues, ensuring fairness, and avoiding bias in AI applications.
  • Future of AI in Healthcare: Trends and emerging technologies that will shape the future of healthcare.
  • Project Presentations: Participants present their projects or AI solution ideas developed during the course.
  • Course Recap and Feedback: Reviewing key concepts, sharing feedback, and discussing next steps for participants to apply their new knowledge.