ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN HEALTHCARE PROGRAMME COURSE DESCRIPTION

Code	Name of the Course Unit	Semester	In-Class Hours (T+P)	Credit	ECTS Credit
TVP255	ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN HEALTHCARE	3	4	2	3

GENERAL INFORMATION
Language of Instruction :	Turkish
Level of the Course Unit :	ASSOCIATE DEGREE, TYY: + 5.Level, EQF-LLL: 5.Level, QF-EHEA: Short Cycle
Type of the Course :	Compulsory
Mode of Delivery of the Course Unit	-
Coordinator of the Course Unit	Lecturer SİBEL SEKMAN
Instructor(s) of the Course Unit
Course Prerequisite	No

OBJECTIVES AND CONTENTS
Objectives of the Course Unit:	This course introduces artificial intelligence (AI) and machine learning (ML) applications in the healthcare field. Students will learn about the basic principles of artificial intelligence, types of algorithms, analysis of health data, the use of AI in diagnostic and treatment support systems, ethical issues, and current case studies.
Contents of the Course Unit:	This course covers topics such as the history and development of artificial intelligence in healthcare, fundamental types of machine learning, algorithm examples (decision trees, regression, clustering), big data and preparing healthcare data for AI, and AI-powered treatment planning systems.

KEY LEARNING OUTCOMES OF THE COURSE UNIT (On successful completion of this course unit, students/learners will or will be able to)
It defines the concepts of artificial intelligence and machine learning.
YZ ve MÖ algoritmalarının temel yapılarını açıklar.
Sağlıkta kullanılan yapay zeka uygulamalarını örneklerle açıklar.
Sağlık verilerinin YZ sistemleriyle analiz süreçlerini değerlendirir.
Yapay zekanın sağlık alanında etik ve hukuki boyutlarını tartışır.

WEEKLY COURSE CONTENTS AND STUDY MATERIALS FOR PRELIMINARY & FURTHER STUDY
Week	Preparatory	Topics(Subjects)	Method
1	Slides and Resource Materials	Introduction to artificial intelligence and machine learning.	Lecture, question-and-answer session, discussion, laboratory application.
2	Slides and Resource Materials	The history and development of artificial intelligence in healthcare.	Lecture, question-and-answer session, discussion, laboratory application.
3	Slides and Resource Materials	Basic types of machine learning (supervised, unsupervised, reinforcement)	Lecture, question-and-answer session, discussion, laboratory application.
4	Slides and Resource Materials	Algorithm examples: Decision trees, regression, clustering.	Lecture, question-and-answer session, discussion, laboratory application.
5	Slides and Resource Materials	Preparing big data and health data for artificial intelligence.	Lecture, question-and-answer session, discussion, laboratory application.
6	Slides and Resource Materials	AI applications in healthcare: Image analysis, diagnostic support systems.	Lecture, question-and-answer session, discussion, laboratory application.
7	Slides and Resource Materials	Artificial intelligence with mobile health apps and smart devices.	Lecture, question-and-answer session, discussion, laboratory application.
8	Slides and Resource Materials	AI-powered treatment planning systems	Lecture, question-and-answer session, discussion, laboratory application.
9	Slides and Resource Materials	Data mining and natural language processing (NLP)	Lecture, question-and-answer session, discussion, laboratory application.
10	-	MID-TERM EXAM	-
11	Slides and Resource Materials	Data privacy and security in artificial intelligence.	Lecture, question-and-answer session, discussion, laboratory application.
12	Slides and Resource Materials	Ethical issues: discrimination, transparency, accountability	Lecture, question-and-answer session, discussion, laboratory application.
13	Slides and Resource Materials	Application examples and case studies	Lecture, question-and-answer session, discussion, laboratory application.
14	Slides and Resource Materials	Overall rating	Lecture, question-and-answer session, discussion, laboratory application.
15	Slides and Resource Materials	Project presentations	Lecture, question-and-answer session, discussion, laboratory application.
16	-	FINAL EXAM	-
17	-	FINAL EXAM	-

SOURCE MATERIALS & RECOMMENDED READING
Medical Informatics and Artificial Intelligence: Biostatistics, Machine Learning and Data Analytics in Healthcare, Assoc. Prof. Dr. Özel SEPETÇİ, Nobel Publishing.
Artificial Intelligence and its Applications in Healthcare, Akademisyen Kitapevi, 2021

ASSESSMENT
Assessment & Grading of In-Term Activities	Number of Activities	Degree of Contribution (%)	Description	Examination Method

Level of Contribution
0	1	2	3	4	5

KNOWLEDGE
Theoretical
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	The student will define the fundamental characteristics of the software, database components, and operating principles used in health information systems.					4
2	It accurately explains the fundamental concepts, building blocks, and terminology specific to the field of health informatics.				3

KNOWLEDGE
Factual
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	It describes the technical concepts, data processing methods, and IT infrastructures used in managing health data.					4

SKILLS
Cognitive
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Within the framework of occupational health and safety, it conducts a technical risk assessment for data processing processes.						5
2	It regularly monitors the latest innovations and developments in the field of health information technology.						5
3	By monitoring technological innovations and current technical applications in their field, they can adapt them to their profession.					4
4	They evaluate developments in their field and effectively use health informatics tools and technological systems.					4

SKILLS
Practical
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	It uses health information systems, database tools, and IT equipment used in data processing applications in an accurate, reliable, and efficient manner.				3
2	She/he develops appropriate solutions to problems that arise in her field.				3
3	They effectively express technical and professional knowledge related to their field through written and oral communication methods.			2
4	They follow scientific and technological developments in the field of health informatics by using a foreign language.					4

OCCUPATIONAL
Autonomy & Responsibility
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Healthcare professionals, IT specialists, and technical teams share their professional knowledge in a clear and understandable manner.				3

OCCUPATIONAL
Learning to Learn
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	It complies with professional ethical rules, data security principles, and relevant legislation.				3

OCCUPATIONAL
Communication & Social
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	By embracing a lifelong learning mindset, it continuously pursues its development in the field of health informatics.				3
2	In the processes of recording, analyzing, and reporting health data, scientific accuracy, ethical principles, and cultural sensitivity are observed.						5

OCCUPATIONAL
Occupational and/or Vocational
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	He/She fully fulfills the technical, administrative, and operational responsibilities required by his/her profession.						5

WORKLOAD & ECTS CREDITS OF THE COURSE UNIT
Workload for Learning & Teaching Activities
Type of the Learning Activites	Learning Activities (# of week)	Duration (hours, h)	Workload (h)
Lecture & In-Class Activities	10	4	40
Preliminary & Further Study	8	2	16
Land Surveying	0	0	0
Group Work	0	0	0
Laboratory	5	2	10
Reading	0	0	0
Assignment (Homework)	0	0	0
Project Work	0	0	0
Seminar	0	0	0
Internship	0	0	0
Technical Visit	0	0	0
Web Based Learning	0	0	0
Implementation/Application/Practice	5	2	10
Practice at a workplace	0	0	0
Occupational Activity	0	0	0
Social Activity	0	0	0
Thesis Work	0	0	0
Field Study	0	0	0
Report Writing	0	0	0
Final Exam	0	0	0
Preparation for the Final Exam	0	0	0
Mid-Term Exam	1	1	1
Preparation for the Mid-Term Exam	0	0	0
Short Exam	1	1	1
Preparation for the Short Exam	0	0	0
TOTAL	30	0	78