HelloAI Professional

For healthcare professionals and executives

1. Welcome to HelloAI
  FREE PREVIEW
2. How to access your learning certificate
3. How to access your ECTS credit
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. 7.6. - 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. HelloAI Live Events Archive
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. HelloAI Module Feedback Survey

About this course

$155.00
125 lessons
29 hours of video content

HelloAI Professional

Welcome to HelloAI ⎢General information

Module 1 | Global overview: AI in Healthcare

Module 2 | Operationalize AI

Module 3 | AI innovation in HCP organization

Module 4 | Intro to Python and Google Colab

Module 5 | Life before AI, traditional image processing

Module 6 | Deep dive in AI

LAB test to qualify for ECTS credits

Module 7 | Foundation models

Module 8 | Advanced Data Management and AI Integration

Module 9 | Patient perception

Module 10 | Scale your solution on a platform

Module 11 | HelloAI Live Events Archive (extra module)

HelloAI Live Event No. 1 | AI in Healthcare: Insights & Impact - 18th September 2024

HelloAI Live Event No. 2 | AI in Healthcare: Innovation & Collaboration - 16th October 2024

Final task ⎢Your feedback

About this course

Welcome to HelloAI ⎢General information

Module 1 | Global overview: AI in Healthcare

Module 2 | Operationalize AI

Module 3 | AI innovation in HCP organization

Module 4 | Intro to Python and Google Colab

Module 5 | Life before AI, traditional image processing

Module 6 | Deep dive in AI​

LAB test to qualify for ECTS credits

Module 7 | Foundation models

Module 8 | Advanced Data Management and AI Integration

Module 9 | Patient perception

Module 10 | Scale your solution on a platform

Module 11 | HelloAI Live Events Archive (extra module)

HelloAI Live Event No. 1 | AI in Healthcare: Insights & Impact - 18th September 2024

HelloAI Live Event No. 2 | AI in Healthcare: Innovation & Collaboration - 16th October 2024

Final task ⎢Your feedback

About this course

Module 6 | Deep dive in AI