Biomedical Data and Artificial Intelligence in Health Sciences
| Title | Βιοϊατρικά Δεδομένα και Τεχνητή Νοημοσύνη στις Επιστήμες Υγείας / Biomedical Data and Artificial Intelligence in Health Sciences |
| Code | ΙΑ2070 |
| Faculty | Health Sciences |
| School | Medicine |
| Cycle / Level | 1st / Undergraduate |
| Teaching Period | Winter |
| Coordinator | Ioanna Chouvarda |
| Common | No |
| Status | Active |
| Course ID | 600022860 |
Programme of Study: UPS of School of Medicine (2019-today)
Registered students: 78
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| KORMOS | Elective Courses | 3 | 2 | 2 |
| Academic Year | 2025 – 2026 |
| Class Period | Winter |
| Faculty Instructors | |
| Weekly Hours | 2 |
| Total Hours | 30 |
| Class ID | 600287324
|
Course Type 2021
Specific Foundation
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600287324
- eLearning: https://elearning.auth.gr/course/view.php?id=8391
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
- Greek (Instruction, Examination)
Prerequisites
General Prerequisites
Introduction to medical informatics. Familiar with Computer applications. If possible, familiar with python or R.
Learning Outcomes
The elective course aims to cover the areas of
A. Medical Data Management (structured and unstructured), quality and standards, medical image analysis, biometric collection and analysis, and decision support applications
B. Machine Learning and Deep Learning, as well as applications with image data, biomarkers, biological, everyday life, and reliability issues of AI in medical decision support applications
Through lectures, demonstrations of technologies and applications, laboratory exercises and group work, students are given the opportunity to:
• Understand the concepts and theory surrounding medical data management issues of technical intelligence.
• Understand the necessary terminologies and the importance of issues related to AI.
• Understand the basic methods of management and analysis in problems based on biomedical data
• Understand the role and importance of AI in problems of medical practice and patient support.
• Become familiar with the use of analysis tools/AI in medical practice issues.
• Become familiar with computational practices in medical procedures and problems.
• Harness and dynamically use AI technologies in medical research and education.
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Work autonomously
- Work in teams
- Work in an international context
- Advance free, creative and causative thinking
Course Content (Syllabus)
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
Keywords
Computational / Artificial Intelligence, biomedical data management
Educational Material Types
- Slide presentations
- Video lectures
- Multimedia
- Interactive excersises
- Book
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
- Use of ICT in Laboratory Teaching
- Use of ICT in Communication with Students
- Use of ICT in Student Assessment
Description
The nature of the course makes the use of Information and Communication technologies necessary in all steps. All exercises take place on PC. Electronic submission of assignments / tests is used.
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 16 | 0.6 | |||
| Laboratory Work | 8 | 0.3 | |||
| Reading Assigment | 6 | 0.2 | |||
| Tutorial | 4 | 0.1 | |||
| Project | 6 | 0.2 | |||
| Written assigments | 4 | 0.1 | |||
| Exams | 12 | 0.4 | |||
| Total | 56 | 2 |
Student Assessment
Student Assessment methods
- Written Exam with Multiple Choice Questions (Summative)
- Written Assignment (Formative, Summative)
- Labortatory Assignment (Formative, Summative)
Last Update
03-09-2025
