HelloAI Professional

HelloAI Professional course

Making AI the superpower of medical professionals

HELLOAI PROFESSIONAL COURSE

Interested in exploring AI and its current and future applications in medical settings? HelloAI Professional is a unique course designed to educate experts within a few weeks on AI technology's context, its potential to enhance patient lives, streamline medical professionals' work, and create new entrepreneurial opportunities.

Who should enrol:

✔ Practicing Medical Professionals ✔ IT Managers ✔ Business Leaders ✔ Senior Healthcare Professionals and Executives ✔ Entrepreneurs

Duration

Self-paced, approximately 10 weeks
Effort

4 hours per week
Content

Theoretical sessions & practical assignments

What will you learn

Understand the outcomes AI can deliver to healthcare provider institutions post initial investment.
Gain insights into when and why you can trust the "Black Box" and comprehend the nature and implications of algorithm bias.
Discover how AI becomes a critical toolkit for making healthcare sustainable and effective through practical case studies.
Explore real-life examples from peer institutions and providers on how AI enhances care quality and effectiveness, including insights on challenges.
Develop a roadmap for addressing key areas within your organization to ensure successful AI implementation.
Understand the benefits and opportunities of data-driven healthcare.
Learn about technology diplomacy to facilitate global alignment of AI governance and foster a vibrant innovation system.

Course curriculum

1. HelloAI Welcome Brochure
2. How to access your ECTS credit
3. How to access your EITH certificate
1. 1.1 - AI, Personalized medicine and rethinking design
2. 1.2. - Radiology powered by AI
3. 1.3. - AI implementation in clinical environment
4. 1.4. - Transforming healthcare with AI
5. 1.5. - AI Application in Ultrasound
1. 2.1. - Outlook: Start-up journey and acceleration, professional speech is important
2. 2.2 - From scienticif idea to product - A startup journey
3. 2.3. - Introduction of LEITAT Technology Center
4. 2.4. - AI Product development cookbook
5. 2.4.1 - GUIDE: Data Science Cookbook
6. Module 2 - Quiz 1 - Data Science Cookbook
7. 2.5 - Outlook: AI from the Lab to the installed Base - Industry insight
1. 3.1. - Introducing AI solution in your Healthcare Provider organization
2. 3.2. - Why Data handling, preparing and distributed machine learning are needed?
3. 3.3. - “Smartreport”– Explaining medical reports with the help of AI
4. 3.4. - AI Insights by UM - D-Lab
5. 3.4.1 - AI in imaging - The Example of handcrafted radiomics by UM
6. 3.4.2 - AI in treatment personalization - by UM
7. 3.4.3 - AI based decision support systems for improved healthcare - By UM
8. 3.5. - Introduction of KTH
1. 4.1. - Python and Google Colab
2. 4.1.1 - GUIDE: Python notebook
3. 4.1.2 - Python and Google Colab - Intro (Python codes included)
4. 4.1.3 - Python and Google Colab - Variables
5. 4.1.4 - Python and Google Colab - Operators
6. 4.1.5 - Python and Google Colab - Data structures
7. 4.1.6 - Python and Google Colab - Controll flow
8. 4.1.7 - Python and Google Colab - Imports
9. 4.1.8 - Python and Google Colab - Functions
10. 4.1.9 - Python and Google Colab - Objects
1. 5.1. - Image Analysis without AI
2. 5.1.1 - Image Analysis without AI - Medical Images
3. 5.1.2 - Image Analysis without AI - Gray-Scale and Texture Features
4. 5.1.3 - Image Analysis without AI - Texture Features (cont)
5. 5.1.4 - Image Analysis without AI - Shape features
1. 6.1. - GUIDE: Quick overview - Basics of ML and AI
2. 6.2. - AI fundamentals
3. 6.2.1 - AI fundamentals - Machine Learning Module
4. 6.2.2 - AI fundamentals - Ontology Logic Module
5. 6.2.3 - AI fundamentals - Deep Learning Module
6. Module 6 - Quiz 1 - AI Fundamentals
7. 6.3. - Machine Learning in Medical Image Analysis
8. 6.3.1 - Machine Learning in Medical Image Analysis - Rule based AI vs machine learning
9. Module 6.3. - Quiz 1 - Machine Learning in Medical Image Analysis
10. 6.3.2 - Machine Learning in Medical Image Analysis - SVM and KNN
11. Module 6.3 - Quiz 2 - Machine Learning in Medical Image Analysis
12. 6.3.3 - Machine Learning in Medical Image Analysis - Decision tree and random forest
13. Module 6.3 - Quiz 3 - Machine Learning in Medical Image Analysis
14. 6.3.4 - Machine Learning in Medical Image Analysis - Image features
15. Module 6.3 - Quiz 4 - Machine Learning in Medical Image Analysis
16. 6.3.5 - Machine Learning in Medical Image Analysis - Machine learning examples
17. Module 6.3 - Quiz 5 - Machine Learning in Medical Image Analysis
18. 6.3.6 - Machine Learning in Medical Image Analysis - Machine learnings vs deep learning
19. Module 6.3 - Quiz 6 - Machine Learning in Medical Image Analysis
20. 6.3.7 - Machine Learning in Medical Image Analysis - ANN
21. Module 6.3 - Quiz 7 - Machine Learning in Medical Image Analysis
22. 6.3.8 - Machine Learning in Medical Image Analysis - CNN for image classification
23. Module 6.3 - Quiz 8 - Machine Learning in Medical Image Analysis
24. 6.3.9 - Machine Learning in Medical Image Analysis - Common CNN Architecture
25. Module 6.3 - Quiz 9 - Machine Learning in Medical Image Analysis
26. 6.3.10 - Machine Learning in Medical Image Analysis - FCN for image segmentation
27. Module 6.3 - Quiz 10 - Machine Learning in Medical Image Analysis
28. 6.3.11 - Machine Learning in Medical Image Analysis - Deep learning examples
29. Module 6.3 - Quiz 11 - Machine Learning in Medical Image Analysis
30. 6.4. - How good is AI for image segmentation and how can you trust it?
31. 6.5. - AI in practice - Laboratory session
32. 6.5.1 - GUIDE: Laboratory Instruction
33. 6.5.2 - AI in practice - Introduction to Colab
34. 6.5.3 - AI in practice - KNN (code included)
35. Module 6.5.3. - Quiz - Deep dive in AI technology - KNN
36. 6.5.4 - AI in practice - SVM (code included)
37. Module 6.5.4 - Quiz - Deep dive in AI technology - SVM
38. 6.5.5 - AI in practice - Random Forest (code included)
39. Module 6.5.5 - Quiz - Deep dive in AI technology - Random Forest
40. 6.5.6 - AI in practice - Feature Extraction (code included)
41. Module 6.5.6 - Quiz- Deep dive in AI technology - Feature Extraction
42. 6.5.7 - AI in practice - Deep Network (code included)
1. Guidelines
1. 7.1.1. -Transformer models
2. 7.1.2. - Transformer architecture
3. 7.1.3. - Vision Transformers and Transformers in Healthcare
4. Module 7.1. - Quiz
5. 7.2. - Large Language Models
6. 7.3. - Text generation
7. 7.4. - Introduction to MONAI and NVIDIA's Contributions to Medical Imaging
8. 7.5.1. - Self-supervised pretraining
9. 7.5.1. - QUIZ -Self-supervised pretraining
10. 7.5.2. - Interactive segmentation with Foundation Models - part 1
11. 7.5.2. - Interactive segmentation with Foundation Models - part 2
12. 7.6. - Introduction to Multi-Modal Foundation Models
1. 8.1. - Health data basics
2. 8.1. - QUIZ - Health data basics
3. 8.2. - Data as the fuel of AI
4. 8.2. - QUIZ -Data as the fuel of AI
5. 8.3. - Challenges and importance of data annotation
6. 8.4. - AI-assisted medical image annotation
7. 8.4. - QUIZ -AI-assisted medical image annotation
8. 8.5. - Introduction to diffusion models
1. 9.1. - Patient-Centric AI
2. 9.2. - Patient perspective, patient communication and your role
3. 9.3. - What are the benefits of AI for patients, patient perceptions
4. 9.4 - Zed Technologies: Patient access to their health data
1. 10.1. - Secure operations verification
2. 10.2. - How Artificial Intelligence is making health care more human
3. 10.3. - Follow my patient AI based solution in a HCP
4. 10.4. - Value and benefits in providing a futuristic scalable roadmap for AI applications
5. 10.5. - Unlock data and unleash intelligence, effortlessly
6. 10.6 - GUIDE: EHS tutorial
7. Module 10 - Quiz - EHS tutorial
1. HelloAI Live Events Archive
1. Please evaluate the HelloAI course

About this course

€399,00
114 lessons
26 hours of video content

Discover the most comprehensive course on AI application in healthcare from the top renowned medical experts, starting today!

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