Overview:

AI is moving from theoretical promise to practical application in healthcare. This course, Applied AI in Healthcare: Diagnosis, Decisions, and Documentation, offers a focused exploration of how AI is enhancing clinical workflows-supporting diagnosis, guiding decision-making, and automating documentation.

Rather than abstract concepts, this course provides real-world tools, examples, and frameworks that healthcare professionals can use to understand and adopt AI safely and effectively in clinical environments.

Why should you Attend:

• Understand the core types of AI used in diagnosis, decision support, and documentation
• Explore real-life applications of AI in clinical settings (e.g., radiology, oncology, primary care)
• Learn how AI improves workflows, accuracy, and efficiency in medical documentation
• Evaluate the opportunities and limitations of AI-driven tools
• Gain practical strategies for assessing, implementing, or collaborating on AI projects in healthcare

• Module 1: The Role of AI in Modern Healthcare
  • What is Applied AI? Overview in plain language
  • Clinical vs. operational AI: key distinctions
  • Why now? Recent advances making AI usable at the bedside
• Module 2: AI in Diagnosis
  • AI in image analysis: radiology, dermatology, pathology
  • Pattern recognition in lab data, ECGs, and genomics
  • Case study: AI-assisted detection of diabetic retinopathy
  • Limitations: false positives/negatives, overreliance
• Module 3: AI in Clinical Decision Support
  • Predictive analytics for early warning systems (e.g., sepsis, deterioration)
  • Risk stratification for triage and treatment decisions
  • Personalized care pathways based on AI insights
  • Case study: AI tools in ICU or oncology decision-making
• Module 4: AI in Documentation
  • NLP and voice-to-text: speeding up clinical note-taking
  • Auto-generating discharge summaries and visit notes
  • AI summarization of patient history for new visits
  • Tools in use: Nuance DAX, Suki, Microsoft Copilot for Healthcare
• Module 5: Challenges, Ethics & Implementation
  • Data privacy, consent, and security
  • Bias in training data and its clinical consequences
  • Regulatory landscape: FDA approvals and guidelines
  • Human-AI collaboration: keeping clinicians in the loop
• Wrap-Up & Q&A
  • Summary of actionable insights
  • Audience questions and scenario discussion
  • Resources for follow-up learning and practical implementation

• Physicians, Surgeons, and Residents
• Nurses, Advanced Practice Providers, and Allied Health Professionals
• Clinical Informatics and EHR Optimization Teams
• Hospital Administrators and Quality Leaders
• Medical Educators and Students Interested in Health Tech