HelloAI Professional RIS Scholarship

HelloAI Professional RIS Scholarship course

Making AI the superpower of medical professionals

HELLOAI PROFESSIONAL RIS SCHOLARSHIP COURSE

HelloAI Professional RIS Scholarship course focuses on advanced AI concepts and the application of customized solutions using various tools and methodologies. Students will advance their skills in scaling AI solutions and effectively presenting them to healthcare organizations.

This EIT Health-labeled program combines theoretical AI presentations with hands-on practical workshops.

Join this course to delve into fascinating and challenging AI topics that you may have heard of but haven't had the opportunity to professionally educate yourself on, further advancing your career.

➡ Make sure to check your eligibility and the conditions of the RIS scholarship here.

Enrol with RIS scholarship!

Full scholarship (administration fee is required) for RIS citizens

To enroll in the HelloAI Professional RIS course with a full scholarship (administration fee required), click the "Register" button below and use the complimentary coupon code at checkout: HELLORISPRO. Eligible professionals can use the HelloAI Professional RIS 90% discount code - and register by paying a 39.9 EUR administration fee.

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30 hrs. of video lectures

Self-paced; enhanced learning experience with visual examples
FULL RIS Scholarship (administration fee is required)

EIT Health supports the HelloAI initiative with scholarships for RIS citizens.
Certification

Each student receives a certificate of completion and the opportunity to apply for ECTS credits.

Who should enrol:

✓ Practising Medical Professionals, ✓ IT Managers, ✓ Business Leaders, ✓ Senior Healthcare Professionals and Executives, ✓ Entrepreneurs

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What will you learn

Insights on the outcomes AI can bring to HCP institutions after the initial investment
When and why can you trust the “Black Box” & understand the nature and implications of algorithm bias
Unveil how AI becomes the critical toolkit in making healthcare sustainable and effective (practical case studies)
See real-life examples of peer institutions and providers on how AI enhances the quality and efficacy of care of successful AI implementation (including insight on challenges)
Discover a roadmap of which areas shall be tackled in your organization to make AI implementation successful
Understand the benefits and opportunities of data-driven healthcare
Learn about technology diplomacy that will ease global alignment of AI governance and 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. 7.5.2. - QUIZ - Interactive segmentation with Foundation models
13. 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. 11.1 - RIS Outlook - The European Healthcare Ecosystem – HVL Roundtable
2. 11.2 - Sycai Technologies - RIS start-up journey, Spain
3. 11.3 - Mindsmiths - RIS start-up journey, Croatia
4. 11.4 - Digital Transformation of Health System – example of Estonia
5. 11.5 - Digital healthcare innovation challenges – IT Infrastructures – patient centered vs Big Data enabling - RIS Insights - Estonia
6. 11.6 - Anthropology of AI - AI communities - RIS, Czech Republic
7. 11.7 - Oncompass - AI for personalized treatment by each type of tumors - RIS start-up journey, Hungary
8. 11.8 - AI in Diabetes – Case study - RIS AI application insight, Greece
9. 11.9 - Start-up support in healthcare innovation? EIT Health!
10. 11.10 - EIT Health RIS – Healthcare Skills and Education
11. 11.11 - EITH, RIS, Alumni Networks - joining the community
1. HelloAI Live Events Archive
1. Please evaluate the HelloAI course

About this course

€399,00
126 lessons
30 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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