Overview:

Artificial Intelligence (AI) is no longer just a buzzword in healthcare it's a rapidly evolving force that's shaping diagnostics, treatment planning, operational efficiency, and patient engagement.

This course takes participants on a journey from the conceptual hype surrounding AI to the practical, evidence-based applications making a difference at the bedside today.

Whether you're a healthcare professional, policymaker, or tech developer, this course demystifies AI, highlights real-world use cases, and equips you with the knowledge to critically evaluate and implement AI solutions in clinical and administrative settings.

Why should you Attend:

• Understand key AI concepts and terminology in the context of healthcare
• Distinguish between hype and validated clinical applications of AI
• Explore real-world case studies where AI is improving patient outcomes
• Identify challenges such as bias, regulation, ethics, and data privacy in AI implementation
• Gain a roadmap for assessing and adopting AI technologies responsibly in healthcare settings

• Module 1: Introduction - AI in Healthcare Today
  • Getting started with ChatGPT in the healthcare context
  • Current state of AI adoption in medicine
  • Separating buzz from reality
• Module 2: Clinical Applications of AI
  • AI in diagnostics (e.g., radiology, pathology, dermatology)
  • Predictive analytics in ICU and emergency settings
  • Virtual health assistants and symptom checkers
  • Case studies: AI in cancer detection, sepsis prediction, and personalized medicine
• Module 3: Operational & Administrative AI
  • AI in scheduling, billing, and revenue cycle optimization
  • NLP for documentation and EHR automation
  • AI chatbots for patient triage and communication
• Module 4: Challenges and Considerations
  • Bias in AI algorithms and health equity risks
  • Data privacy and compliance (HIPAA, GDPR)
  • Regulatory landscape: FDA and AI as a medical device
  • Interpretable AI and the "black box" problem
• Module 5: Roadmap to Implementation
  • How to evaluate AI vendors and tools
  • Key steps in pilot testing and scaling
  • Building cross-functional AI readiness in hospitals and clinics
• Wrap-Up and Q&A
  • Resources for continued learning
  • Discussion and real-world insights

• Healthcare professionals (clinicians, nurses, radiologists, pathologists, etc.) interested in AI applications in practice
• Hospital administrators and health IT leaders evaluating AI tools
• Medical students and public health researchers exploring digital transformation
• Health tech developers and data scientists working with clinical data
• Policy advisors and regulators concerned with ethical and practical implications of AI in medicine
• No prior programming or AI knowledge is required this course is designed to be accessible and practical