Introduction
Artificial Intelligence is rapidly transforming healthcare, from diagnostic imaging to personalized treatment plans. This course provides an in-depth exploration of how AI is applied in clinical and administrative settings. Participants will examine medical datasets, predictive models, and ethical challenges unique to healthcare. Through hands-on sessions, learners will practice applying AI tools to realistic medical scenarios. By the end, attendees will understand the opportunities and responsibilities involved in healthcare AI.
Course Objectives
- Understand AI use cases in healthcare
- Explore medical data and preprocessing techniques
- Study predictive and diagnostic models
- Identify ethical and regulatory constraints
- Apply AI tools to medical scenarios
Target Audience
- Healthcare professionals
- Data analysts and ML engineers
- Medical researchers
- Students in health technology programs
- IT professionals in healthcare
Course Outline
- 5 Sections
- 0 Lessons
- 5 Days
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- Day 1: Healthcare AI Overview• Healthcare system needs
• AI in diagnostics and operations
• Medical data formats
• HIPAA and privacy considerations
• Case studies0 - Day 2: Working With Medical Data• Structured vs. unstructured data
• Imaging data basics
• Data annotation challenges
• Bias in medical datasets
• Hands-on: Medical data preprocessing0 - Day 3: Predictive Modeling in Healthcare• Risk prediction models
• Time-series models
• Diagnostic classifiers
• Evaluation techniques
• Hands-on: Predictive modeling0 - Day 4: Medical Imaging AI• CNNs for imaging
• Detection and segmentation
• Transfer learning for radiology
• False positives/negatives
• Hands-on: Build an imaging model0 - Day 5: Responsible Healthcare AI• Clinician oversight
• Explainability requirements
• Regulatory compliance
• Patient safety risks
• Capstone project0
Instructor
Lessons:
