Physiotherapy and Rehabilitation
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

General course introduction information

Course Code:

FTR446

Course Name:

Applications of Artificial İntelligence and Motor Learning in Healthcare

Course Semester:

Spring

Course Credits:

Theoretical	Practical	Credit	ECTS
2	0	2	2

Language of instruction:

Course Requisites:

Does the Course Require Work Experience?:

Type of course:

Department/Faculty/University

Course Level:

Bachelor

TR-NQF-HE:6. Master`s Degree

QF-EHEA:First Cycle

EQF-LLL:6. Master`s Degree

Mode of Delivery:

Face to face

Course Coordinator :

Dr.Öğr.Üyesi KÜBRA KENDAL

Course Lecturer(s):

Course Assistants:

Course Objective and Content

Course Objectives:

These objectives are aimed at the role of physiotherapy studies in the development of marketing of artificial intelligence basic concepts and applications in the healthcare sector, especially in the registration of physiotherapy. Students will learn a variety of artificial intelligence tools and technologies that are revolutionizing patient care, diagnosis, treatment planning, and personalized medicine.

Course Content:

This lesson; Introduction and importance of Artificial Intelligence in Health, Artificial Intelligence in Physiotherapy, Future of Artificial Intelligence in Physiotherapy and motor learning; Includes topics.

Learning Outcomes

The students who have succeeded in this course;

Learning Outcomes

1 - Knowledge
Theoretical - Conceptual
1) Understands the basic principles and algorithms of artificial intelligence in healthcare.
2) Recognizes artificial intelligence applications in diagnosis, treatment planning and patient monitoring in physiotherapy.
2 - Skills
Cognitive - Practical
3 - Competences
Communication and Social Competence
Learning Competence
Field Specific Competence
1) Evaluates the impact of artificial intelligence on patient outcomes and clinical decision-making in physiotherapy.
Competence to Work Independently and Take Responsibility

Lesson Plan

Week	Subject	Related Preparation
1)	Introduction and Importance of Artificial Intelligence in Health
2)	Fundamentals of Machine Learning
3)	Data in Health: Governance and Ethical Considerations
4)	Artificial Intelligence in Medical Imaging and Diagnosis
5)	Natural Language Processing in Healthcare Documentation
6)	Predictive Analytics in Patient Care
7)	Robotics and Artificial Intelligence in Physiotherapy
8)	Midterm Exam
9)	Wearable Technology and Patient Monitoring
10)	Telehealth and Artificial Intelligence
11)	Ethical Impacts of Artificial Intelligence in Health
12)	Legal Aspects and Data Privacy in Healthcare Solutions
13)	Case Studies: Artificial Intelligence Success Stories in Physiotherapy
14)	The Future of Artificial Intelligence in Physiotherapy
15)	The Place of Motor Learning in Artificial Intelligence
16)	Final Exam

Sources

Course Notes / Textbooks:	Haftalık verilecektir.
References:	Kumar, A., Ahirwal, M. K., & Londhe, N. D. (2022). Sağlık Bakımı için Yapay Zeka Uygulamaları. CRC Press.

Course-Program Learning Outcome Relationship

Learning Outcomes	1	2	3
Program Outcomes
1) Physiotherapy profession, knowledge of basic medical sciences, clinical information on diseases and has a knowledge of foreign languages.
3) Adopts the principles of professional ethics and patient rights
5) Involves in the protective scope of rehabilitation services, applies health practices to improve individual life quality.

Course - Learning Outcome Relationship

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	Physiotherapy profession, knowledge of basic medical sciences, clinical information on diseases and has a knowledge of foreign languages.
3)	Adopts the principles of professional ethics and patient rights
5)	Involves in the protective scope of rehabilitation services, applies health practices to improve individual life quality.

Learning Activity and Teaching Methods

Expression
Lesson
Q&A / Discussion

Assessment & Grading Methods and Criteria

Written Exam (Open-ended questions, multiple choice, true-false, matching, fill in the blanks, sequencing)

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Midterms	1	% 40
Final	1	% 60
total		% 100
PERCENTAGE OF SEMESTER WORK		% 40
PERCENTAGE OF FINAL WORK		% 60
total		% 100

Workload and ECTS Credit Grading

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	14	2	28
Study Hours Out of Class	16	2	32
Midterms	1	2	2
Final	1	2	2
Total Workload			64