Information Package / Course Catalogue
Artificial Intelligence and Health
Course Code: TGT275
Course Type: Non Departmental 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

The aim of this course is to enable students to understand the fundamental concepts of artificial intelligence technologies, their current application areas in healthcare, clinical decision support systems, large language models, and their ethical and legal aspects; and to gain the knowledge and awareness to use artificial intelligence tools in healthcare in a conscious, safe, and responsible manner.

Course Content

Introduction to artificial intelligence, machine learning and generative AI concepts, digital transformation in healthcare, electronic health records, clinical decision support systems, medical image analysis, large language models (ChatGPT, etc.), data analytics in healthcare, personalized medicine, remote healthcare services, robotic applications, ethical principles, data security, personal data protection, artificial intelligence legislation, and future working models for healthcare professionals using artificial intelligence.

Name of Lecturer(s)
Learning Outcomes
1.Explains the basic concepts of artificial intelligence and its applications in the health field
2.Explains the use of artificial intelligence-powered healthcare technologies in diagnosis, treatment, and patient care processes
3.Explains ethical principles, data security, and fundamental approaches to patient privacy in artificial intelligence applications
4.Explains the key features of using generative artificial intelligence tools in education, research, and healthcare
5.Explains the opportunities, limitations, and future development areas offered by artificial intelligence in healthcare
Recommended or Required Reading
1.Artificial Intelligence in Healthcare Bohr, A., & Memarzadeh, K. (Eds.). (2020). Artificial Intelligence in Healthcare. Academic Press.
2.Artificial Intelligence in Medicine Ranschaert, E., Morozov, S., & Algra, P. R. (Eds.). (2023). Artificial Intelligence in Medicine. Springer.
Weekly Detailed Course Contents
Week 1 - Theoretical
Introduction to artificial intelligence
Week 2 - Theoretical
Applications of artificial intelligence in healthcare: Diagnosis, treatment, patient monitoring, medical imaging, laboratory, e-health, and clinical decision support systems
Week 3 - Theoretical
AI-based medical imaging and clinical decision support applications in healthcare
Week 4 - Theoretical
Generative artificial intelligence tools: Effective and safe use of tools like ChatGPT, Gemini, Copilot, and others in the healthcare field; prompt generation applications.
Week 5 - Theoretical
AI in scientific research: AI applications in literature review, academic writing, summarizing, data analysis, and presentation preparation
Week 6 - Theoretical
Ethical and legal aspects: AI ethics, patient privacy, GDPR, data security, algorithmic bias, and accountability
Week 7 - Theoretical
AI-powered clinical decision-making processes and sample case applications
Week 8 - Theoretical
Review of the key concepts covered in the course/Midterm Exam
Week 9 - Theoretical
Artificial intelligence and medical imaging: AI applications and example systems in radiology, pathology, and other imaging fields
Week 10 - Theoretical
Personalized medicine and precision healthcare applications: Genomic data, biomarkers, and the role of artificial intelligence in individualized treatments
Week 11 - Theoretical
Artificial intelligence applications in telehealth and remote patient monitoring systems
Week 12 - Theoretical
Robotics and the future of healthcare technologies: Surgical robots, care robots, digital assistants, and autonomous healthcare systems
Week 13 - Theoretical
AI applications in healthcare: Current national and international examples, success stories, limitations, and future trends; student applications
Week 14 - Theoretical
Overall review: The impact of artificial intelligence on healthcare professions, a reassessment of ethics and professional responsibilities, and a general review of the course
Assessment Methods and Criteria
Type of AssessmentCountPercent
Attending Lectures1%5
Quiz1%5
Midterm Examination1%30
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory141242
Quiz1112
Midterm Examination1213
Final Examination1213
TOTAL WORKLOAD (hours)50
Contribution of Learning Outcomes to Programme Outcomes
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1
5
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2
2
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2
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2
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OÇ-5
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1
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3
Adnan Menderes University - Information Package / Course Catalogue
2026