| Course Type | Elective Course |
|---|
| Course Level | Associate Degree |
|---|
| Year of Study | - |
|---|
| Semester | - |
|---|
| ECTS | 3 |
|---|
| Name of Lecturer(s) |
|
| Learning Outcomes |
At the end of this course, the students;
- Explains the fundamental concepts of artificial intelligence and machine learning
- Defines the application areas of artificial intelligence in healthcare with examples. Introduces the basic machine learning models used in patient data analysis, diagnosis, and treatment processes.
- Interprets the impacts of artificial intelligence?based decision support systems in healthcare services; discusses ethical, security, and privacy aspects.
- Follows current artificial intelligence applications in healthcare and evaluates artificial intelligence?based practices.
|
| Mode of Delivery | Face to face |
|---|
| Prerequisites | No |
|---|
| Recommended Optional Programme Components | |
|---|
| Course Description | This course aims to introduce the fundamental principles and application areas of artificial intelligence (AI) and machine learning technologies in healthcare services. The course covers the use of AI and machine learning approaches in areas such as healthcare data types and analysis, computer-aided diagnosis systems, treatment planning, medical image processing, and patient monitoring systems. Ethical issues, data security, and patient privacy are also discussed from the perspective of artificial intelligence. |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
Introduction to Artificial Intelligence: Basic Concepts, Historical Development, and Working Principles |
|
2nd Week |
Artificial Intelligence in Healthcare and Its Applications |
|
3rd Week |
Introduction to Machine Learning and Basic Concepts |
|
4th Week |
Working and Learning Processes of Machine Learning Models |
|
5th Week |
Introduction to Deep Learning and Artificial Neural Networks |
|
6th Week |
Natural Language Processing (NLP) and Its Applications in Healthcare |
|
7th Week |
Machine Learning and Deep Learning Applications in Health Data Analysis |
|
8th Week |
Midterm Examination |
|
9th Week |
Generative Artificial Intelligence (Generative AI): Basic Concepts, Applications, and Areas of Use |
|
10th Week |
Artificial Intelligence Ethics, Data Security, and Patient Privacy |
|
11th Week |
Project Presentations I |
|
12th Week |
Project Presentations II |
|
13th Week |
Project Presentations III |
|
14th Week |
General Review and Evaluation of Project Presentations |
|
| Recommended or Required Reading | |
|---|
| Planned Learning Activities and Teaching Methods |
- Lecture
- Discussion
- Questions/Answers
- Presentation
|
|---|
| Assessment Methods and Criteria |
|
Quantity |
Percentage (%) |
| Mid-term |
1 |
30 |
| Assignment |
1 |
20 |
| Total(%) |
|
50 |
| Contribution of In-term Studies to Overall Grade(%) |
|
50 |
| Contribution of Final Examination to Overall Grade(%) |
|
50 |
| Total(%) |
|
100 |
|
| ECTS Allocated Based on Student Workload |
| Activities |
Quantity |
Duration (Hour) |
Total Workload (Hour) |
| Midterm exam |
1 |
1 |
1 |
| Preparation for Quiz |
|
|
|
| Individual or group work |
|
|
|
| Preparation for Final exam |
1 |
30 |
30 |
| Course hours |
14 |
2 |
28 |
| Preparation for Midterm exam |
1 |
20 |
20 |
| Laboratory (including preparation) |
|
|
|
| Final exam |
1 |
1 |
1 |
| Homework |
1 |
10 |
10 |
| Total Work Load | 90 |
| Total Work Load / 30 (h) | 3 |
| ECTS Credit of the Course | 3 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
|---|
