Biomedical Data and Artificial Intelligence in Health Sciences

This course offers students with a medical background the opportunity to familiarize themselves with Artificial Intelligence by using different types of data — such as biomedical signals, images, and clinical data — and to practice developing predictive pipelines to address various clinical questions.

This is an elective course in the BSc program offered by the School of Medicine at Aristotle University of Thessaloniki.

Course Content 

• Introduction - general concepts - data science and ML - data exploration and visualization
• Medical Data Management, quality and standards
• Machine Learning – Theory – Lab
• Deep learning –Theory–lab
• AI & Medical Decision Support/Ethics and trustworthiness of AI
• Medical image analysis and segmentation/characterization applications
• Medical Imaging, Radiomics & AI in diagnosis and prognosis
• Biomedical Signals - Biosignal collection and analysis
• Patient Decision Support / Decision Support and Behavioral Informatics
• AI Applications (Clinical Data, Biomarkers, Biological Data)
• TN in the management of the patient's everyday life

Learning Outcomes

The elective course aims to cover the areas of:

1.  Medical Data Management, data quality and standards, medical image analysis, biosignal collection and analysis, and decision support applications.
2. Machine Learning and Deep Learning, including applications involving image data, biosignals, biological data, and daily life data, as well as issues related to the reliability of Artificial Intelligence in medical decision support applications.

Through lectures, demonstrations of technologies and applications, laboratory exercises, and group projects, students will have the opportunity to:

• Understand the concepts and theory related to medical data management and AI.
• Become familiar with the necessary terminology and the importance of topics related to AI.
• Comprehend the basic methods of managing and analyzing problems based on biomedical data.
• Understand the role and significance of AI in medical practice and patient support.
• Become familiar with the use of analytical and AI tools in medical practice.
• Become familiar with computational practices in medical procedures and problems.

• Utilize and dynamically apply AI technologies in medical research and education.

  General Prerequisites

  General knowledge of medical informatics and basic skills in computer applications. Familiarity with programming languages such as MATLAB or R is desirable.