HelloAI Advanced RIS Scholarship

HelloAI Advanced RIS Scholarship course

Delivering exponential growth in AI expertise to medical professionals.

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HELLOAI ADVANCED RIS SCHOLARSHIP COURSE

Does ML and AI still remain a black box to you? AI has the power to assist medical experts in diagnosing patients by analyzing symptoms, automating operational and administrative tasks, suggesting personalized treatments, and predicting risks. To foster AI adoption in healthcare, clinicians need comprehensive training to fully harness its benefits.

According to the Stanford Medicine Report, 47% of physicians and 73% of medical students are actively seeking additional training or classes to better prepare for healthcare innovations. They anticipate almost one-third of their job to be automated by 2040.

The HelloAI Advanced RIS Scholarship course explores various medical applications of AI, addressing a wide range of challenges with impactful practical examples, from accelerating innovation to real clinical implementations. The course includes technical exercises for deep dives into AI and introduces digital platforms that enable innovation, development, and secure data storage while ensuring data privacy. HelloAI Advanced RIS Scholarship course also emphasizes patient-centric care, illustrating how AI can humanize healthcare by freeing clinicians to engage more deeply with patients on a personal level.

How to register with an EIT Health RIS Scholarship

To enroll in the HelloAI Advanced RIS Scholarship course with a full scholarship (administration fee required), click any 'Enroll' button and use promo code HELLORISADVANCED at checkout. Registering implies acceptance of the RIS Scholarship conditions.

CHECK Conditions

Make sure to check your eligibility and the conditions of the RIS Scholarship.

The course spans 10 weeks but allows for self-paced learning. Limited scholarship seats are available; early enrollment is advised.

Who should apply?

Ideal for: MSc and PhD students, early career professionals and researchers, budding entrepreneurs (ideas, MVPs, early stage), individuals passionate about AI in healthcare (no specific background required), and those committed to completing the full course.

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22+ hrs. of video lectures

Self-paced; improved learner experience with visual examples.
FULL RIS Scholarship (administration fee is requested)

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

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

What to expect

Generate new ideas about the existing technologies and how those can improve depending on future needs.
Explore new AI solutions, development and application in the public healthcare system in a new perspective.
Understand the process of the development of AI in a public health system which requires laying the foundations for proper data management and protection.
Connect with credible, specialized organizations and individuals in AI, facilitating the possibility of possible collaboration in future projects.

What are the benefits

Earn up to 7,5 ECTS credits
Access to global network of medical experts & professors
Explore new career opportunities and a chance to launch a business in the HC AI domain
Interact with topmost experts and like-minded peers
Learn about the HC AI ecosystem – players, roles and opportunities
Access to a broad set of materials to match your interest

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. - Health data basics
2. 7.1. - QUIZ - Health data basics
3. 7.2. - Data as the fuel of AI
4. 7.2. - QUIZ -Data as the fuel of AI
5. 7.3. - Challenges and importance of data annotation
1. 8.1 - RIS Outlook - The European Healthcare Ecosystem – HVL Roundtable
2. 8.2 - Sycai Technologies - RIS start-up journey, Spain
3. 8.3 - Mindsmiths - RIS start-up journey, Croatia
4. 8.4 - Digital Transformation of Health System – example of Estonia
5. 8.5 - Digital healthcare innovation challenges – IT Infrastructures – patient centered vs Big Data enabling - RIS Insights - Estonia
6. 8.6 - Anthropology of AI - AI communities - RIS, Czech Republic
7. 8.7 - Oncompass - AI for personalized treatment by each type of tumors - RIS start-up journey, Hungary
8. 8.8 - AI in Diabetes – Case study - RIS AI application insight, Greece
9. 8.9 - Start-up support in healthcare innovation? EIT Health!
10. 8.10 - EIT Health RIS – Healthcare Skills and Education
11. 8.11 - EITH, RIS, Alumni Networks - joining the community
1. Welcome & Introduction – Taha-Kass-Hout Global Chief Science & Technology Officer GE HealthCare
2. Fire-side chat with Prof. Dr. med. Mathias Goyen & Jan Beger moderated by William Benko
3. Operational AI for healthcare: Predicting Missed Care Opportunities Suzannah E. McKinney, MBChB
4. Q&A with Suzannah E. McKinney
5. AI-Driven Transformation in Radiology: Enhanced Efficiency and Operational Excellence – Dr. Peter Strouhal, Alliance Medical
6. Q&A with Dr. Peter Strouhal
7. Wrap up by Prof. Dr. med. Mathias Goyen
1. Introduction & Interview with Parry Bathia, GE Chief AI Officer
2. AI in prostate cancer diagnosis - dr. Anthony Rix
3. AI use case:Human out of the loop - Prof. Dr. Felix Nensa
4. The importance of clinical collaboration in AI development - Shannon Beach
1. Please evaluate the HelloAI course

About this course

€199,00
109 lessons
26.5 hours of video content

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

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