Information Package / Course Catalogue
Artificial Intelligence Applications in Radiology
Course Code: TGT153
Course Type: Area Elective
Couse Group: Short Cycle (Associate's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 2
Prt.: 0
Credit: 2
Lab: 0
ECTS: 2
Objectives of the Course

This course aims to teach students the fundamentals of artificial intelligence in radiology and demonstrate how this technology is applied in medical imaging processes.

Course Content

This course introduces students to the fundamental concepts of artificial intelligence and machine learning techniques in radiology. It discusses how artificial intelligence is applied in medical imaging with examples, and demonstrates to students how to process and analyze medical images using these technologies. Additionally, it addresses the ethical and legal dimensions of artificial intelligence applications, aiming to make students aware and responsible in this field.

Name of Lecturer(s)
Lec. Muhammed HASDEMİR
Learning Outcomes
1.Students will understand the concepts of artificial intelligence and machine learning, and grasp the fundamental applications of these technologies in radiology.
2.Students will learn about imaging techniques and image processing, classification, and analysis methods using artificial intelligence.
3.They will learn solutions related to real patient and imaging problems using various medical imaging techniques.
4.They will grasp the current developments in artificial intelligence and radiology.
5.They will understand the ethical and legal boundaries of artificial intelligence applications in medical imaging.
Recommended or Required Reading
1.Deep Learning for Medical Image Analysis, 2023, S. Kevin Zhou, Hayit Greenspan, Dinggang Shen
2.Machine and Deep Learning in Oncology, Medical Physics and Radiology, 2022, Issam El Naqa, Martin J. Murphy
Weekly Detailed Course Contents
Week 1 - Theoretical
Fundamental Concepts of Artificial Intelligence and Machine Learning
Week 2 - Theoretical
History and Foundations of Artificial Intelligence
Week 3 - Theoretical
Intersection Points of Radiology and Artificial Intelligence
Week 4 - Theoretical
Fundamentals of Digital Image Processing
Week 5 - Theoretical
Image Segmentation, Classification, and Feature Extraction
Week 6 - Theoretical
Planning Student Projects
Week 7 - Theoretical
Deep Learning Models Used in Medical Image Analysis
Week 8 - Theoretical
Introduction of Student Projects/Midterm exemination
Week 9 - Theoretical
Ethical and Legal Issues in Artificial Intelligence Applications
Week 10 - Theoretical
Software and Tools Used for Artificial Intelligence and Image Processing
Week 11 - Theoretical
Implementation of Artificial Intelligence in Clinical Settings
Week 12 - Theoretical
Current Research and Developments in Artificial Intelligence
Week 13 - Theoretical
The Future of Artificial Intelligence in Radiology
Week 14 - Theoretical
Case Studies Using Various Radiological Images
Assessment Methods and Criteria
Type of AssessmentCountPercent
Midterm Examination1%40
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory141242
Midterm Examination1112
Final Examination1516
TOTAL WORKLOAD (hours)50
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
PÇ-8
PÇ-9
PÇ-10
PÇ-11
PÇ-12
OÇ-1
3
5
2
OÇ-2
3
5
2
5
2
OÇ-3
2
3
4
2
2
2
5
3
OÇ-4
3
5
5
OÇ-5
5
4
3
3
Adnan Menderes University - Information Package / Course Catalogue
2026