Generative AI in Healthcare: Transforming Bedside Outcomes

1h 40mBeginner2025-03-21

Authors

Zain Khalpey, MD, PhD, FACS

Course details

The healthcare landscape is being transformed by AI, creating both opportunities and challenges for medical professionals. This course bridges the gap between AI theory and clinical practice, offering healthcare practitioners a practical understanding of AI implementation at the bedside. Dr. Zain Khalpey explores real-world applications of AI in healthcare, from predictive analytics to clinical decision support systems. Through case studies drawn from actual clinical settings, learn to evaluate AI tools, understand their limitations, and identify opportunities for implementation in your practice. The course covers topics like machine learning fundamentals, deep learning in medical imaging, predictive modeling for patient outcomes, and ethical considerations in medical AI.

Gain practical insights into integrating AI solutions while maintaining high standards of patient care, and understand AI's transformative potential in healthcare and have the knowledge to evaluate and implement AI tools effectively in clinical settings.

Skills covered

Digital TransformationTech FoundationsIT Service ManagementAI for Business FoundationsBusiness StrategyDevOpsArtificial Intelligence for BusinessNetwork and System AdministrationCloud ComputingBusiness Analysis and StrategyLeadership and ManagementSoftware DevelopmentOne-Off

Concepts

0. Introduction

01 - Welcome to the AI revolution in healthcare

1. AI and Machine Learning - Healthcare Applications

02 - Supervised and unsupervised learning
03 - Healthcare problems that AI can address
04 - Case study - AI applications in healthcare

2. AI for Disease Diagnosis and Patient Monitoring

05 - AI in diagnosis - Real-world applications
06 - AI in medical imaging - Enhancing diagnostic accuracy
07 - AI in remote patient monitoring
08 - Case studies - AI success stories in diagnostics

3. Natural Language Processing and Data Analytics in Healthcare

09 - Understanding natural language processing (NLP)
10 - NLP applications in medical literature and pathology reports
11 - The role of AI in healthcare data analysis
12 - NLP in clinical documentation - From unstructured to actionable

4. Interpretability in Machine Learning - Benefits and Challenges

13 - How to understand interpretability in machine learning - A beginner s overview
14 - The importance of transparency in healthcare AI
15 - Challenges in achieving interpretability
16 - Techniques for interpretable machine learning in healthcare

5. Patient Risk Stratification and Augmenting Clinical Workflows

17 - Identifying high-risk patients with AI
18 - The role of AI in augmenting clinical workflows
19 - Potential benefits and challenges in real-life settings
20 - Case study - AI in clinical workflow optimization

6. Cultivating Trust in Healthcare AI

21 - The importance of trust in healthcare AI
22 - Strategies for building trust - Transparency and education
23 - Engaging healthcare professionals and patients with AI
24 - Ethical considerations in healthcare AI

Conclusion

25 - Continuing your AI in healthcare journey