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1.1 AI, Personalized Medicine, and Rethinking Design

1.2 Radiology Powered by AI

1.3 AI Implementation in Clinical Environments

1.4 Transforming Healthcare with AI

1.5 AI Applications in Ultrasound

2.1 Outlook: Startup Journey and the Importance of Professional Communication

2.2 From Scientific Idea to Product – A Startup Journey

2.3 Introduction to LEITAT Technology Center

2.4 AI Product Development Cookbook

2.4.1 Guide: Data Science Cookbook

Module 2 Quiz: Data Science Cookbook

2.5 Outlook: AI from the Lab to the Installed Base – Industry Insight

3.1 Introducing AI Solutions in Your Healthcare Provider Organization

3.2 Why Data Handling, Preparation, and Distributed Machine Learning Matter

3.3 "SmartReport" – Explaining Medical Reports with AI

3.4 AI Insights by UM D-Lab

3.4.1 AI in Imaging – Handcrafted Radiomics (UM)

3.4.2 AI in Treatment Personalization (UM)

3.4.3 AI-Based Decision Support Systems (UM)

3.5 Introduction to KTH

4.1 Python and Google Colab

4.1.1 Guide: Python Notebook

4.1.2 Python Code Introduction

4.1.3 Variables

4.1.4 Operators

4.1.5 Data Structures

4.1.6 Control Flow

4.1.7 Imports

4.1.8 Functions

4.1.9 Objects

5.1 Image Analysis Without AI

5.1.1 Medical Images

5.1.2 Gray-Scale and Texture Features

5.1.3 Texture Features (Continued)

5.1.4 Shape Features

6.1 Guide: Quick Overview – Basics of Machine Learning and AI

6.2 AI Fundamentals

6.2.1 Machine Learning

6.2.2 Ontology Logic

6.2.3 Deep Learning

Module 6 Quiz: AI Fundamentals

6.3 Machine Learning in Medical Image Analysis

6.3.1 Rule-Based AI vs Machine Learning

6.3.2 SVM and KNN

6.3.3 Decision Trees and Random Forests

6.3.4 Image Features

6.3.5 Machine Learning Examples

6.3.6 Machine Learning vs Deep Learning

6.3.7 Artificial Neural Networks (ANN)

6.3.8 Convolutional Neural Networks (CNN)

6.3.9 Common CNN Architectures

6.3.10 Fully Convolutional Networks (FCN)

6.3.11 Deep Learning Examples

Quizzes 1–11: Machine Learning in Medical Image Analysis

6.4 Evaluating AI for Image Segmentation and Trustworthiness

6.5 AI in Practice – Laboratory Sessions

6.5.1 Guide: Laboratory Instructions

6.5.2 Introduction to Colab

6.5.3 KNN (code included)

Module 6.5.3. - Quiz - Deep dive in AI technology - KNN

6.5.4 SVM (code included)

Module 6.5.4 - Quiz - Deep dive in AI technology - SVM

Random Forest (code included)

Module 6.5.5 - Quiz - Deep dive in AI technology - Random Forest

6.5.6 Feature Extraction (code included)

Module 6.5.6 - Quiz- Deep dive in AI technology - Feature Extraction

6.5.7 Deep Network (code included)

Guidelines

8.1 Health Data Basics

Quiz: Health Data Basics

8.2 Data as the Fuel of AI

Quiz: Data as the Fuel of AI

8.3 Challenges and Importance of Data Annotation

• Welcome & Introduction – Dr. Taha Kass-Hout, GE HealthCare

• Fireside Chat – Prof. Dr. Mathias Goyen & Jan Beger (Moderator: William Benko)

• Predicting Missed Care Opportunities – Dr. Suzannah McKinney

• Q&A with Dr. McKinney

• AI-Driven Transformation in Radiology – Dr. Peter Strouhal

• Q&A with Dr. Strouhal

• Wrap-Up – Prof. Dr. Mathias Goyen

• Interview with Parry Bathia, GE HealthCare

• AI in Prostate Cancer Diagnosis – Dr. Anthony Rix

• Human Out of the Loop Use Case – Prof. Dr. Felix Nensa

• Importance of Clinical Collaboration in AI Development – Shannon Beach

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