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Duration 14 hours
Course Outline
Introduction to Edge AI in Medicine
- Overview of Edge AI and its importance in healthcare
- Major advantages and obstacles of deploying Edge AI in medical settings
- Emerging trends and innovations in healthcare Edge AI
- Practical applications and real-world case studies
Wearable Technology and Edge AI
- Introduction to wearable health devices and their capabilities
- Creating AI models for wearable health tracking
- Collecting and processing data on wearable devices
- Illustrative examples and case studies
Diagnostic Instruments and Edge AI
- Utilizing Edge AI for diagnostic imaging and analysis
- Integrating AI models into diagnostic hardware
- Improving diagnostic precision and efficiency with Edge AI
- Case studies demonstrating Edge AI in diagnostics
Patient Monitoring Systems
- Designing real-time patient monitoring systems with Edge AI
- Data management and processing in patient monitoring
- Connecting Edge AI with healthcare IoT devices
- Practical deployment and case studies
Creating AI Models for Medical Use
- Survey of relevant machine learning and deep learning models
- Training and optimizing models for edge deployment
- Tools and frameworks for healthcare Edge AI (TensorFlow Lite, OpenVINO, etc.)
- Model validation and assessment in clinical environments
Deploying Edge AI in Healthcare
- Procedure for deploying AI models on healthcare edge devices
- Real-time data processing and inference on edge hardware
- Monitoring and managing deployed healthcare AI models
- Practical deployment examples and case studies
Ethical and Regulatory Frameworks
- Safeguarding data privacy and security in healthcare Edge AI
- Mitigating bias and ensuring fairness in healthcare AI models
- Adherence to healthcare regulations and standards (HIPAA, GDPR, etc.)
- Best practices for responsible AI deployment in medicine
Performance Evaluation and Tuning
- Methods for assessing model performance on healthcare edge devices
- Tools for real-time monitoring and debugging
- Strategies for optimizing AI model performance in healthcare
- Overcoming latency, reliability, and scalability challenges
Innovative Applications and Use Cases
- Advanced applications of Edge AI in healthcare
- Deep-dive case studies in telemedicine, personalized medicine, and beyond
- Success stories and key takeaways
- Future trends and opportunities in healthcare Edge AI
Practical Projects and Exercises
- Building a comprehensive Edge AI application for healthcare
- Real-world projects and scenarios
- Collaborative group exercises
- Project presentations and feedback sessions
Conclusion and Future Pathways
Requirements
- Solid grasp of AI and machine learning principles
- Proficiency in programming languages (Python preferred)
- Knowledge of medical technologies and systems
Target Audience
- Medical professionals
- Biomedical engineers
- AI developers
Testimonials (2)
skills and projects in claude, variety of tools for different purpose
Agnieszka Czypionka
Course - Generative AI in Healthcare: Transforming Medicine and Patient Care
Comprehensiveness about the backround; how to do a prompt.