Pre-registration is open!

To pre-register for the HelloAI Professional RIS course for free, click on the "Enrol" button, use the special promo code: HELLORISPRO and reserve your seat today!

Please note that this course is complimentary for RIS residents with a small admission fee of only 10 euros.

Your application will be evaluated by the Organizers, and access to the course will be activated based on your eligibility.  Course materials will be available from the 20th of May until the 30th of August, 2022. 

 ➡  Check your eligibility for the RIS scholarship here

Who should enrol:

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

  • Duration

    Self-paced/ approx. 10 weeks

  • Effort

    4 hours/week

  • Content

    Theoretical sessions & practical assignments

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

    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  ​​

    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. - Challenges and importance of data annotation​

    1. 3.1. - Introducing AI solution in your Healthcare Provider organization

    2. 3.2. - Education, innovation and accelerator funding opportunities​​

    3. 3.3. - “Smartreport”– Explaining medical reports with the help of AI ​

    4. 3.4. - Secure operations verification

    5. 3.5. - GUIDE: Quick overview - Basics of ML and AI

    6. 3.6. - Introduction of KTH

    7. 3.7. - AI fundamentals ​

    8. 3.7.1 - AI fundamentals - Machine Learning Module

    9. 3.7.2 - AI fundamentals - Ontology Logic Module

    10. 3.7.3 - AI fundamentals - Deep Learning Module

    11. Module 3 - Quiz 1 - AI Fundamentals

    12. 3.8. - Python and Google Colab ​

    13. 3.8.1 - GUIDE: Python notebook

    14. 3.8.2 - Python and Google Colab - Intro (Python codes included)

    15. 3.8.3 - Python and Google Colab - Variables

    16. 3.8.4 - Python and Google Colab - Operators

    17. 3.8.5 - Python and Google Colab - Data structures

    18. 3.8.6 - Python and Google Colab - Controll flow

    19. 3.8.7 - Python and Google Colab - Imports

    20. 3.8.8 - Python and Google Colab - Functions

    21. 3.8.9 - Python and Google Colab - Objects

    1. 4.1. - Why Data handling, preparing and distributed machine learning are needed?​

    2. 4.2. - Image Analysis without AI ​

    3. 4.2.1 - Image Analysis without AI - Medical Images

    4. 4.2.2 - Image Analysis without AI - Gray-Scale and Texture Features

    5. 4.2.3 - Image Analysis without AI - Texture Features (cont)

    6. 4.2.4 - Image Analysis without AI - Shape features

    7. 4.3. - Machine Learning in Medical Image Analysis ​

    8. 4.3.1 - Machine Learning in Medical Image Analysis ​- Rule based AI vs machine learning

    9. Module 4 - Quiz 1 - Machine Learning in Medical Image Analysis - Lesson 1

    10. 4.3.2 - Machine Learning in Medical Image Analysis ​- SVM and KNN

    11. Module 4 - Quiz 1 - Machine Learning in Medical Image Analysis - Lesson 2

    12. 4.3.3 - Machine Learning in Medical Image Analysis ​- Decision tree and random forest

    13. Module 4 - Quiz 1 - Machine Learning in Medical Image Analysis - Lesson 3

    14. 4.3.4 - Machine Learning in Medical Image Analysis ​- Image features

    15. Module 4 - Quiz 1 - Machine Learning in Medical Image Analysis - Lesson 4

    16. 4.3.5 - Machine Learning in Medical Image Analysis ​- Machine learning examples

    17. Module 4 - Quiz 1 - Machine Learning in Medical Image Analysis - Lesson 5

    18. 4.3.6 - Machine Learning in Medical Image Analysis ​- Machine learnings vs deep learning

    19. Module 4 - Quiz 1 - Machine Learning in Medical Image Analysis - Lesson 6

    20. 4.3.7 - Machine Learning in Medical Image Analysis ​- ANN

    21. Module 4 - Quiz 1 - Machine Learning in Medical Image Analysis - Lesson 7

    22. 4.3.8 - Machine Learning in Medical Image Analysis ​- CNN for image classification

    23. Module 4 - Quiz 1 - Machine Learning in Medical Image Analysis - Lesson 8

    24. 4.3.9 - Machine Learning in Medical Image Analysis ​- Common CNN Architecture

    25. Module 4 - Quiz 1 - Machine Learning in Medical Image Analysis - Lesson 9

    26. 4.3.10 - Machine Learning in Medical Image Analysis ​- FCN for image segmentation

    27. Module 4 - Quiz 1 - Machine Learning in Medical Image Analysis - Lesson 10

    28. 4.3.11 - Machine Learning in Medical Image Analysis ​- Deep learning examples

    29. Module 4 - Quiz 1 - Machine Learning in Medical Image Analysis - Lesson 11

    30. 4.4 - AI Insights by UM - D-Lab

    31. 4.4.1 - AI in imaging - The Example of handcrafted radiomics by UM

    32. 4.4.2 - AI in treatment personalization - by UM

    33. 4.4.3 - AI based decision support systems for improved healthcare - By UM

    34. 4.4.4 - How good is AI for image segmentation and how can you trust it?​

    35. 4.5. - AI in practice - Laboratory session ​

    36. 4.5.1 - GUIDE: Laboratory Instruction

    37. 4.5.2 - AI in practice - Introduction to Colab

    38. 4.5.3 - AI in practice - KNN (code included)

    39. Module 4 - Quiz 2 - Deep dive in AI technology - Lesson 1 - KNN

    40. 4.5.4 - AI in practice - SVM (code included)

    41. Module 4 - Quiz 2 - Deep dive in AI technology - Lesson 2 - SVM

    42. 4.5.5 - AI in practice - Random Forest (code included)

    43. Module 4 - Quiz 2 - Deep dive in AI technology - Lesson 3 - Random Forest

    44. 4.5.6 - AI in practice - Feature Extraction (code included)

    45. Module 4 - Quiz 2 - Deep dive in AI technology - Lesson - Feature Extraction

    46. 4.5.7 - AI in practice - Deep Network (code included)

    47. 4.6 - Outlook: AI from the Lab to the installed Base - Industry insight

    1. 5.1. - How Artificial Intelligence is making health care more human​​

    2. 5.2. - You need a platform: Edison Digital Platform as start-up co-development model​​

    3. 5.3. - Follow my patient AI based solution in a HCP​​

    4. 5.4. - Value and benefits in providing a futuristic scalable roadmap for AI applications ​​

    5. Module 5 - Quiz 1 - Introduction Edison orchestrator

    6. 5.5. - Unlock data and unleash intelligence, effortlessly​

    7. 5.6 - GUIDE: EHS tutorial

    8. Module 5 - Quiz 3 - EHS tutorial

About this course

  • €399,00
  • 103 lessons
  • 27.5 hours of video content

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