-Management of Healthcare Organizations

-Financial resources

-Manage the complexity of the implementation of AI-based activities

-Provide support to decision-making process in a multi-objective environment

-Acceptance of AI by healthcare professionals/managing change;

-Redesigning roles and systems;

-Use of AI in Education and Training;

-Patients safety and clinical governance considerations;

-Who has primacy - doctor or machine? Medical-legal aspects;

-AI and the clinician patient relationship - interacting with expert patients, potential disempowerment of clinicians, potential to devalue clinical roles;

-New roles/professions in healthcare - bioinformaticians, data managers, informatics;

-Social and psychological aspects of computer-mediated communication.

-Information modeling (files, databases)

-Data in healthcare (biological, clinical, administrative and research)

-Electronic data collection

-Interoperability

-Descriptive statistics

-Univariate analysis

-Bivariate analysis

-Inferential statistics

-Introduction to data science. Typical problems and applications. Introduction to supervised and unsupervised learning.

-Introduction to techniques of data mining and knowledge discovery in databases, with emphasis on their application in medicine. Data preprocessing, visualizations (types and appropriate use).

-Data clustering techniques, cluster explanation.

-Dimensionality reduction techniques, projections.

-Predictive models: classification, regression.

-Overfitting.

-Model evaluation.

-Explanations of predictive models, SHAP values.

-Practical examples of data science from medicine, bioinformatics, and healthcare.

-Introduction to the EU framework for Trustworthy AI

-The Z-Inspection® process

-Assessment of AI use cases in healthcare

-Assessment of (digital) health technologies

-Framework for achieving Trustworthy AI

-Trustworthy AI: principles and measurement

-Statistical learning models

- Machine learning models

-Accuracy

-Robustness

-Explainability

-Fairness

-HTA principles

-Implementation of HTA in different healthcare systems

-AI assessment

-Quality in Healthcare Organizations

-Performance Management

-Financial Management in Health

-Commissioning and Licensing

-Project management

-Leadership in Healthcare

-International competition and cross-border healthcare services

-What is a programming language and what can it be used for;

-Python essential syntax

-Variables and data structures: basic data types, strings, tuples,  lists, and dictionaries;

-Control structures: conditionals, loops, functions;

-Intro to Object Oriented Programming: classes, objects and methods;

-Leveraging external libraries: installing, importing and usage

Image processing; Image classification

-Multi-layer perceptrons + gradient descent

-Deep learning

-Convolutional neural networks and advanced architectures

-Object detection

-Image Segmentation

-Recurrent neural networks

-Video Analysis

-Search, MDPs, CSPs

- introduction to probability theory and Bayes' Nets, -Decision Networks

-Value of Perfect Information

Reinforcement Learning

-HMMs

-Particle Filtering and Machine Learning

-Hyperparameter Optimization,

-Neural Architecture Search,

-Bayesian optimization,

-Evolutionary algorithms,

-Multi-fidelity optimization and gradient-based optimization,

-Useful meta strategies for speeding up the learning itself or AutoML

-Dealing with unstructured data in healthcare
-Text preprocessing, concordances and collocations

-Clustering and cluster exploration on medical texts

-Word enrichment and keyword techniques

-Vector presentation of documents

-Predictive modeling on text data

-Topic modeling

-Semantic analysis and document summarization

-Sentiment analysis

Module A

-What is information ethics? Why is it useful?

-Introduction to ethical theories and frameworks.

-Information ethics applied to specific issues, e.g., human rights, information access, privacy, cybersecurity, etc.

-Scholarly and media literature on generally discussed/documented issues with AI/ML, including AI/ML causing/being used in ethically problematic situations with a progressive focus on medical applications.  

-Thought experiments and trolley problems, whose reasoned analysis will draw on information ethics principles.

Module B

-Digital Rights and Data ownership

-Right to privacy and its legislation (GDPR)

-Informed consent and patient autonomy

-Legal design techniques in health

-Data-driven decisions in health and AI and actors liability

-Re-use of personal data in healthcare and research

-Medical Device Regulation