COURSE SYLLABUS

ARTIFICIAL INTELLIGENCE IN MEDICINE

1	Course Title:	ARTIFICIAL INTELLIGENCE IN MEDICINE
2	Course Code:	TIP3191
3	Type of Course:	Optional
4	Level of Course:	First Cycle
5	Year of Study:	3
6	Semester:	5
7	ECTS Credits Allocated:	3
8	Theoretical (hour/week):	1
9	Practice (hour/week) :	0
10	Laboratory (hour/week) :	0
11	Prerequisites:	None
12	Recommended optional programme components:	None
13	Language:	Turkish
14	Mode of Delivery:	Face to face
15	Course Coordinator:	Öğr. Gör. Dr. Mevlüt Okan AYDİN
16	Course Lecturers:	Prof.Dr.Züleyha Alper Doç.Dr.İlker Mustafa Kafa
17	Contactinformation of the Course Coordinator:	Ögr. Gör. Dr. Okan Aydın okanaydin@uludag.edu.tr Uludağ Üniversitesi Tıp Fakültesi, USIM, 16059 Nilüfer, Bursa
18	Website:	http://bilgipaketi.uludag.edu.tr/Programlar/Detay/28?AyID=30
19	Objective of the Course:	Students will be able to; accelerate the processes such as diagnosis and treatment in the clinic and to increase the service assurance of all human malpractice.
20	Contribution of the Course to Professional Development	History of artificial intelligence, how to use artificial intelligence, artificial intelligence and malpractice, preparing scenarios with artificial intelligence and reinterpreting data using it,

Learning Outcomes:

1	Has knowledge of the basic techniques and methods of artificial intelligence.;
2	Have knowledge of what machine learning systems are and how they work.;
3	Interpret the patient's complaints and symptoms;
4	Can combine and interpret the tests performed with the patient's data.;
5	They can reinterpret the patient's data with artificial intelligence and confirm its accuracy.;
6	They can use artificial intelligence for these purposes and verify its accuracy.;
7	Discuss the ethics of the use of AI in the medical field.;
8	Students can solve a scenario constructed with AI and confirm its accuracy.;

22	Course Content:

Week	Theoretical	Practical
1	Introduction to artificial intelligence
2	Machine learning systems
3	Decide with clinical information (practice)
4	Clinical decision support systems (practice)
5	AI-based clinical decision making (practice)
6	Artificial intelligence in medical diagnosis, treatment selection and monitoring
7	Artificial intelligence and ethics
8	Use of artificial intelligence in health (practice)
9
10
11
12
13
14

23	Textbooks, References and/or Other Materials:	1.Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong - Mathematics For Machine Learning (2019, Cambridge University Press) https://mml-book.github.io/ 2.Linear Algebra and Its Applications, 5th Edition, David C.Lay, Stephen R. Lay, Judi J.McDonald. 3. Calculus, A complete course, 9th edition, Robert A.Adams & Christopher Essex. 4. Master Algoritma: Yapay Zeka hayatımızı nasıl değiştirecek? Pedro Domingos, 5.https://eithealth.eu/wp-content/uploads/2020/03/EIT-Health-and-McKinsey_Transforming-Healthcare-with-AI.pdf 6.https://www.nature.com/articles/s41746-021-00385-9 7.https://ieeexplore.ieee.org/abstract/document/9676651 8.https://aapm.onlinelibrary.wiley.com/doi/full/10.1002/mp.13562 9.https://ieeexplore.ieee.org/abstract/document/9429985 10.https://www.nature.com/articles/s41746-020-0221-y 11.https://www.mdpi.com/2077-0383/11/8/2265 12.ÖZKAN, Y. ve EROL, Ç., 2018, "Kanser Biyoenformatiğinde Yapay Zeka", ISBN: 978-605-9594-54-7, Papatya Yayıncılık Eğitim A.Ş., İstanbul.
24	Assesment

TERM LEARNING ACTIVITIES	NUMBER	PERCENT
Midterm Exam	1	40
Quiz	0	0
Homeworks, Performances	0	0
Final Exam	1	60
Total	2	100
Contribution of Term (Year) Learning Activities to Success Grade		40
Contribution of Final Exam to Success Grade		60
Total		100
Measurement and Evaluation Techniques Used in the Course	Measurement and evaluation are carried out according to the principles of Bursa Uludağ University Associate Degree and Undergraduate Education and Training Regulation.
Information

ECTS / WORK LOAD TABLE

CONTRIBUTION OF LEARNING OUTCOMES TO PROGRAMME QUALIFICATIONS

LO: Learning Objectives

PQ: Program Qualifications

Contribution Level:

1 Very Low

2 Low

3 Medium

4 High

5 Very High