HelloAI Advanced RIS course
EIT Health - RIS Scholarship course - Delivering exponential growth in AI expertise to medical professionals
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. ️
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!
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.
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
HelloAI Welcome Brochure
How to access your ECTS credit
How to access your EITH certificate
1.1 - AI, Personalized medicine and rethinking design
1.2. - Radiology powered by AI
1.4. - Transforming healthcare with AI
2.1 - Outlook: Start-up journey and acceleration, professional speech is important
2.2 - From scienticif idea to product - A startup journey
2.3. - Introduction of LEITAT Technology Center
2.4. - AI Product development cookbook
2.4.1 - GUIDE: Data Science Cookbook
Module 2 - Quiz 1 - Data Science Cookbook
2.5. - Challenges and importance of data annotation
3.1. - Introducing AI solution in your Healthcare Provider organization
3.2. - Education, innovation and accelerator funding opportunities
3.3. - “Smartreport”– Explaining medical reports with the help of AI
3.4. - Secure operations verification
3.5 - GUIDE: Quick overview - Basics of ML and AI
3.6 - Introduction of KTH
3.7. - AI fundamentals
3.7.1 - AI fundamentals - Machine Learning Module
3.7.2 - AI fundamentals - Ontology Logic Module
3.7.3 - AI fundamentals - Deep Learning Module
Module 3 - Quiz 1 - AI Fundamentals
3.8. - Python and Google Colab
3.8.1 - GUIDE: Python notebook
3.8.2 - Python and Google Colab - Intro (Python codes included)
3.8.3 - Python and Google Colab - Variables
3.8.4 - Python and Google Colab - Operators
3.8.5 - Python and Google Colab - Data structures
3.8.6 - Python and Google Colab - Controll flow
3.8.7 - Python and Google Colab - Imports
3.8.8 - Python and Google Colab - Functions
3.8.9 - Python and Google Colab - Objects
4.1. - Why Data handling, preparing and distributed machine learning are needed?
4.2. - Image Analysis without AI
4.2.1 - Image Analysis without AI - Medical Images
4.2.2 - Image Analysis without AI - Gray-Scale and Texture Features
4.2.3 - Image Analysis without AI - Texture Features (cont)
4.2.4 - Image Analysis without AI - Shape features
4.3. - Machine Learning in Medical Image Analysis
4.3.1 - Machine Learning in Medical Image Analysis - Rule based AI vs machine learning
Module 4 - Quiz 1 - Machine Learning in Medical Image Analysis - Lesson 1
4.3.2 - Machine Learning in Medical Image Analysis - SVM and KNN
Module 4 - Quiz 1 - Machine Learning in Medical Image Analysis - Lesson 2
4.3.3 - Machine Learning in Medical Image Analysis - Decision tree and random forest
Module 4 - Quiz 1 - Machine Learning in Medical Image Analysis - Lesson 3
4.3.4 - Machine Learning in Medical Image Analysis - Image features
Module 4 - Quiz 1 - Machine Learning in Medical Image Analysis - Lesson 4
4.3.5 - Machine Learning in Medical Image Analysis - Machine learning examples
Module 4 - Quiz 1 - Machine Learning in Medical Image Analysis - Lesson 5
4.3.6 - Machine Learning in Medical Image Analysis - Machine learnings vs deep learning
Module 4 - Quiz 1 - Machine Learning in Medical Image Analysis - Lesson 6
4.3.7 - Machine Learning in Medical Image Analysis - ANN
Module 4 - Quiz 1 - Machine Learning in Medical Image Analysis - Lesson 7
4.3.8 - Machine Learning in Medical Image Analysis - CNN for image classification
Module 4 - Quiz 1 - Machine Learning in Medical Image Analysis - Lesson 8
4.3.9 - Machine Learning in Medical Image Analysis - Common CNN Architecture
Module 4 - Quiz 1 - Machine Learning in Medical Image Analysis - Lesson 9
4.3.10 - Machine Learning in Medical Image Analysis - FCN for image segmentation
Module 4 - Quiz 1 - Machine Learning in Medical Image Analysis - Lesson 10
4.3.11 - Machine Learning in Medical Image Analysis - Deep learning examples
Module 4 - Quiz 1 - Machine Learning in Medical Image Analysis - Lesson 11
4.4 - AI Insights by UM - D-Lab
4.4.1 - AI in imaging - The Example of handcrafted radiomics by UM
4.4.2 - AI in treatment personalization - by UM
4.4.3 - AI based decision support systems for improved healthcare - By UM
4.4.4 - How good is AI for image segmentation and how can you trust it?
4.5. - AI in practice - Laboratory session
4.5.1 - GUIDE: Laboratory Instruction
4.5.2 - AI in practice - Introduction to Colab
4.5.3 - AI in practice - KNN (code included)
Module 4 - Quiz 2 - Deep dive in AI technology - Lesson 1 - KNN
4.5.4 - AI in practice - SVM (code included)
Module 4 - Quiz 2 - Deep dive in AI technology - Lesson 2 - SVM
4.5.5 - AI in practice - Random Forest (code included)
Module 4 - Quiz 2 - Deep dive in AI technology - Lesson 3 - Random Forest
4.5.6 - AI in practice - Feature Extraction (code included)
Module 4 - Quiz 2 - Deep dive in AI technology - Lesson - Feature Extraction
4.5.7 - AI in practice - Deep Network
4.6 - Outlook: AI from the Lab to the installed Base - Industry insight
Guidelines