HelloAI Advanced RIS - SEATS STILL AVAILABLE

Does ML and AI still remain a black box to you? AI has the power to help medical experts in diagnosing patients by analysing symptoms, automating operational and administrative tasks, suggesting personalized treatments, and predicting risk. To foster the adoption of AI in Healthcare clinicians need to be fully immersed and trained to get its true benefit. 

47% of physicians and 73% of medical students said that they are currently seeking out additional training or classes to better prepare themselves for innovations in healthcare expecting almost one-third of their job to be automated by 2040 - according to the Stanford Medicine Report.

The HelloAI Advanced course highlights various medical aspects where AI can be applied and solve a wide range of challenges with several impactful practical examples from acceleration and innovation to first-hand experiences of real clinical implementations. The course includes technical exercises to deep dive into AI and introduced digital platforms that enable innovation, development, and storage of data while providing security and data privacy. HelloAI Advanced also pays high attention to patient perception: how AI can make healthcare human again by freeing up time for clinicians to interact with their patients on a human level. ️ 

How to register with an EIT Health RIS Full-Scholarship

To register for the HelloAI Advanced RIS course with a full scholarship, click on any Enrol button and use the special promo code: HELLORIS at the checkout. By registering you will automatically accept the conditions of the RIS Scholarship.

CHECK Conditions

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

The deadline for applications is 31st October and course materials are available until the 31st of December, 2022.  

The course is designed as a 10-week learning experience, but students are free to choose their own pace. There are limited seats available for scholarship opportunities - make sure to enrol soon!

Who should apply?

 ✓ MSc, PhD students   ✓ Early career professionals and researchers  ✓ Youngling Entrepreneurs (ideas, MVPs, early stage)  ✓ People with a strong interest in Al in Healthcare (no specific background required)  ✓ Individuals who are passionate about AI and are committed to completing the full course

ENROL NOW

  • 22+ hrs. of video lectures

    Self-paced; improved learner experience with visual examples.

  • Full Scholarship

    EIT Health supports the HelloAI initiative with full 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. 1.4. - 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

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

    1. Guidelines

About this course

  • €199,00
  • 127 lessons
  • 31 hours of video content

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