Course Code: generativeaihealth
Duration: 21 hours
Prerequisites:
  • Basic understanding of machine learning concepts
  • Familiarity with Python programming
  • Introductory knowledge of biology and healthcare systems

Audience

  • Healthcare professionals
  • Data analysts
  • Policy makers
Overview:

Generative AI is a transformative technology that leverages machine learning models to generate new, synthetic instances of data that can be used for a variety of applications in healthcare.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level healthcare professionals, data analysts, and policy makers who wish to understand and apply generative AI in the context of healthcare.

By the end of this training, participants will be able to:

  • Explain the principles and applications of generative AI in healthcare.
  • Identify opportunities for generative AI to enhance drug discovery and personalized medicine.
  • Utilize generative AI techniques for medical imaging and diagnostics.
  • Assess the ethical implications of AI in medical settings.
  • Develop strategies for integrating AI technologies into healthcare systems.

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.

Course Customization Options

  • To request a customized training for this course, please contact us to arrange.
Course Outline:

Introduction to Generative AI

  • Understanding AI and machine learning fundamentals
  • Deep dive into generative models
  • The landscape of generative AI in healthcare

Generative AI in Drug Discovery

  • Accelerating drug design with AI
  • Case studies: Success stories and challenges
  • Virtual screening and predictive models

Personalized Medicine through Generative AI

  • Tailoring treatments with AI
  • Genomics and AI: A new era of personalization
  • Ethical considerations in AI-powered personalized medicine

Advancements in Medical Imaging

  • Enhancing diagnostics with generative AI
  • 3D medical imaging and AI reconstruction techniques
  • Improving patient outcomes with AI-assisted imaging

Real-World Applications and Future Directions

  • Integrating generative AI into clinical practice
  • The future of AI in patient care and management
  • Final project: Proposing an AI solution for a healthcare challenge

Ethical and Societal Implications

  • Navigating the ethical landscape of AI in healthcare
  • Data privacy, security, and governance
  • Preparing for the future: Policy and regulation

Summary and Next Steps

Sites Published:

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