Course Detail
Course Detail
Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|---|---|---|---|---|
| ARTIFICIAL INTELLIGENCE APPLICATIONS in HEALTH SCIENCES | ERG2116485 | Fall Semester | 2+0 | 2 | 3 |
| Course Program |
| Prerequisites Courses | |
| Recommended Elective Courses |
| Language of Course | Turkish |
| Course Level | First Cycle (Bachelor's Degree) |
| Course Type | Elective |
| Course Coordinator | Lect. Abdullah Furkan CANGİ |
| Name of Lecturer(s) | Lect. Abdullah Furkan CANGİ |
| Assistant(s) | Lecturer Abdullah Furkan Cangi |
| Aim | This course aims to integrate health sciences and artificial intelligence (AI) by enabling students to understand fundamental AI approaches used in healthcare, work with health-related data, and develop awareness of AI-supported clinical decision-making systems. The course also promotes critical thinking in ethical, privacy, and security aspects of AI in healthcare. |
| Course Content | This course contains; Relationship between artificial intelligence and health sciences,Future and potential impacts of AI in healthcare,Basic concepts and algorithms of artificial intelligence,Importance and impact of AI applications in healthcare,Diagnosis and treatment follow-up with AI,Use of classification and regression algorithms in health data,Unsupervised learning methods,Deep learning,Disease risk prediction and prevention strategies,Midterm Exam,Privacy and security of health data,AI-supported clinical decision-making in occupational therapy,AI-based intervention planning and tracking in occupational therapy,Final Exam. |
| Course Learning Outcomes | Teaching Methods | Assessment Methods |
| Explains artificial intelligence concepts and their relation to health sciences. | A, E | |
| Applies basic AI algorithms to health-related datasets. | D, E | |
| Analyzes and maps classification, regression, and deep learning models to health-related problems. | A, F | |
| Interprets the structure and logic of clinical decision support systems. | A, E | |
| Discusses ethical, privacy, and data security principles in AI applications. | D, E | |
| Evaluates the potential impact of artificial intelligence in healthcare delivery. | A, E | |
| Develops example scenarios for AI-supported decision-making in occupational therapy practice. | F, H |
| Teaching Methods: | |
| Assessment Methods: | A: Traditional Written Exam, D: Oral Exam, E: Homework, F: Project Task, H: Performance Task |
Course Outline
| Order | Subjects | Preliminary Work |
|---|---|---|
| 1 | Relationship between artificial intelligence and health sciences | Topol, E. (2019). Deep Medicine: How AI Can Make Healthcare Human Again. Hachette UK. |
| 1 | Future and potential impacts of AI in healthcare | Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347-1358. |
| 2 | Basic concepts and algorithms of artificial intelligence | Bohr, A., & Memarzadeh, K. (Eds.). (2020). Artificial intelligence in healthcare. Academic Press. |
| 3 | Importance and impact of AI applications in healthcare | Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., ... & Wang, Y. (2017). Artificial intelligence in healthcare: past, present and future. Stroke and vascular neurology, 2(4). |
| 4 | Diagnosis and treatment follow-up with AI | Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., ... & Dean, J. (2019). A guide to deep learning in healthcare. Nature medicine, 25(1), 24-29. |
| 5 | Use of classification and regression algorithms in health data | Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347-1358. |
| 6 | Unsupervised learning methods | Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., & Yang, G. Z. (2016). Deep learning for health informatics. IEEE journal of biomedical and health informatics, 21(1), 4-21. |
| 7 | Deep learning | LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444. |
| 8 | Disease risk prediction and prevention strategies | LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444. |
| 9 | Midterm Exam | Choi, E., Bahadori, M. T., Schuetz, A., Stewart, W. F., & Sun, J. (2016, December). Doctor ai: Predicting clinical events via recurrent neural networks. In Machine learning for healthcare conference (pp. 301-318). PMLR. |
| 10 | Privacy and security of health data | Choi, E., Bahadori, M. T., Schuetz, A., Stewart, W. F., & Sun, J. (2016, December). Doctor ai: Predicting clinical events via recurrent neural networks. In Machine learning for healthcare conference (pp. 301-318). PMLR. |
| 12 | AI-supported clinical decision-making in occupational therapy | Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347-1358. |
| 13 | AI-based intervention planning and tracking in occupational therapy | LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444. |
| 14 | Final Exam | Choi, E., Bahadori, M. T., Schuetz, A., Stewart, W. F., & Sun, J. (2016, December). Doctor ai: Predicting clinical events via recurrent neural networks. In Machine learning for healthcare conference (pp. 301-318). PMLR. |
| Resources |
| Bohr, A., & Memarzadeh, K. (2020). Artificial Intelligence in Healthcare. Academic Press. Topol, E. (2019). Deep Medicine: How AI Can Make Healthcare Human Again. Hachette UK. |
| Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347–1358. Esteva, A., et al. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24–29. |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications | |||||||
| No | Program Qualification | Contribution Level | |||||
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | PQ-1. Knows how to reach current basic, theoretical and applied scientific knowledge in the field of occupational therapy by using information technologies and resources; evaluates the accuracy, reliability and validity of this information. | X | |||||
| 2 | PQ-2. Uses accurate assessment methods for individuals and communities in terms of activity and participation, plans therapy and applies it within the scope of evidence-based occupational therapy theory and foundations. | X | |||||
| 3 | PQ-3. Describes a person's nature, needs and performance in relation to daily life, production, leisure activities and tasks, and the relationship between activity and health and well-being. | X | |||||
| 4 | PQ-4. Works in a person-centered manner by interpreting activity and participation limitations and using activities in prevention, rehabilitation and treatment. | X | |||||
| 5 | PQ-5. Carries out his/her professional and academic studies effectively and ethically, has the ability to work independently and actively within and between disciplines. | X | |||||
| 6 | PQ-6. Within the framework of social responsibility awareness, determines needs in research, projects and activities related to occupational therapy science, creates relevant research questions, researches independently and continues lifelong learning. | X | |||||
| 7 | PQ-7. Uses information resources effectively by adopting the features of adapting to new conditions, learning, developing new ideas, giving importance to quality throughout life. | X | |||||
| 8 | PQ-8. Determines personal and professional learning needs, learns at least one foreign language, develops a positive attitude towards lifelong learning and demonstrates what she has learned. | X | |||||
| 9 | PQ-10. Expresses herself effectively by using information and communication technologies related to the field of occupational therapy and establishing verbal and written communication. | X | |||||
| 10 | PQ-10. In the development of occupational therapy, acts in accordance with the legal regulations, scientific and professional ethical values that concern his field as an individual; The client fulfills the responsibilities required by his professional performance, protects and defends his professional rights by observing his rights. | X | |||||
Assessment Methods
| Contribution Level | Absolute Evaluation | |
| Rate of Midterm Exam to Success | 40 | |
| Rate of Final Exam to Success | 60 | |
| Total | 100 | |
| ECTS / Workload Table | ||||||
| Activities | Number of | Duration(Hour) | Total Workload(Hour) | |||
| Course Hours | 14 | 2 | 28 | |||
| Guided Problem Solving | 0 | 0 | 0 | |||
| Resolution of Homework Problems and Submission as a Report | 0 | 0 | 0 | |||
| Term Project | 0 | 0 | 0 | |||
| Presentation of Project / Seminar | 0 | 0 | 0 | |||
| Quiz | 0 | 0 | 0 | |||
| Midterm Exam | 4 | 2 | 8 | |||
| General Exam | 14 | 3 | 42 | |||
| Performance Task, Maintenance Plan | 0 | 0 | 0 | |||
| Total Workload(Hour) | 78 | |||||
| Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30*=(78/30) | 3 | |||||
| ECTS of the course: 30 hours of work is counted as 1 ECTS credit. | ||||||
Detail Informations of the Course
Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|---|---|---|---|---|
| ARTIFICIAL INTELLIGENCE APPLICATIONS in HEALTH SCIENCES | ERG2116485 | Fall Semester | 2+0 | 2 | 3 |
| Course Program |
| Prerequisites Courses | |
| Recommended Elective Courses |
| Language of Course | Turkish |
| Course Level | First Cycle (Bachelor's Degree) |
| Course Type | Elective |
| Course Coordinator | Lect. Abdullah Furkan CANGİ |
| Name of Lecturer(s) | Lect. Abdullah Furkan CANGİ |
| Assistant(s) | Lecturer Abdullah Furkan Cangi |
| Aim | This course aims to integrate health sciences and artificial intelligence (AI) by enabling students to understand fundamental AI approaches used in healthcare, work with health-related data, and develop awareness of AI-supported clinical decision-making systems. The course also promotes critical thinking in ethical, privacy, and security aspects of AI in healthcare. |
| Course Content | This course contains; Relationship between artificial intelligence and health sciences,Future and potential impacts of AI in healthcare,Basic concepts and algorithms of artificial intelligence,Importance and impact of AI applications in healthcare,Diagnosis and treatment follow-up with AI,Use of classification and regression algorithms in health data,Unsupervised learning methods,Deep learning,Disease risk prediction and prevention strategies,Midterm Exam,Privacy and security of health data,AI-supported clinical decision-making in occupational therapy,AI-based intervention planning and tracking in occupational therapy,Final Exam. |
| Course Learning Outcomes | Teaching Methods | Assessment Methods |
| Explains artificial intelligence concepts and their relation to health sciences. | A, E | |
| Applies basic AI algorithms to health-related datasets. | D, E | |
| Analyzes and maps classification, regression, and deep learning models to health-related problems. | A, F | |
| Interprets the structure and logic of clinical decision support systems. | A, E | |
| Discusses ethical, privacy, and data security principles in AI applications. | D, E | |
| Evaluates the potential impact of artificial intelligence in healthcare delivery. | A, E | |
| Develops example scenarios for AI-supported decision-making in occupational therapy practice. | F, H |
| Teaching Methods: | |
| Assessment Methods: | A: Traditional Written Exam, D: Oral Exam, E: Homework, F: Project Task, H: Performance Task |
Course Outline
| Order | Subjects | Preliminary Work |
|---|---|---|
| 1 | Relationship between artificial intelligence and health sciences | Topol, E. (2019). Deep Medicine: How AI Can Make Healthcare Human Again. Hachette UK. |
| 1 | Future and potential impacts of AI in healthcare | Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347-1358. |
| 2 | Basic concepts and algorithms of artificial intelligence | Bohr, A., & Memarzadeh, K. (Eds.). (2020). Artificial intelligence in healthcare. Academic Press. |
| 3 | Importance and impact of AI applications in healthcare | Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., ... & Wang, Y. (2017). Artificial intelligence in healthcare: past, present and future. Stroke and vascular neurology, 2(4). |
| 4 | Diagnosis and treatment follow-up with AI | Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., ... & Dean, J. (2019). A guide to deep learning in healthcare. Nature medicine, 25(1), 24-29. |
| 5 | Use of classification and regression algorithms in health data | Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347-1358. |
| 6 | Unsupervised learning methods | Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., & Yang, G. Z. (2016). Deep learning for health informatics. IEEE journal of biomedical and health informatics, 21(1), 4-21. |
| 7 | Deep learning | LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444. |
| 8 | Disease risk prediction and prevention strategies | LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444. |
| 9 | Midterm Exam | Choi, E., Bahadori, M. T., Schuetz, A., Stewart, W. F., & Sun, J. (2016, December). Doctor ai: Predicting clinical events via recurrent neural networks. In Machine learning for healthcare conference (pp. 301-318). PMLR. |
| 10 | Privacy and security of health data | Choi, E., Bahadori, M. T., Schuetz, A., Stewart, W. F., & Sun, J. (2016, December). Doctor ai: Predicting clinical events via recurrent neural networks. In Machine learning for healthcare conference (pp. 301-318). PMLR. |
| 12 | AI-supported clinical decision-making in occupational therapy | Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347-1358. |
| 13 | AI-based intervention planning and tracking in occupational therapy | LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444. |
| 14 | Final Exam | Choi, E., Bahadori, M. T., Schuetz, A., Stewart, W. F., & Sun, J. (2016, December). Doctor ai: Predicting clinical events via recurrent neural networks. In Machine learning for healthcare conference (pp. 301-318). PMLR. |
| Resources |
| Bohr, A., & Memarzadeh, K. (2020). Artificial Intelligence in Healthcare. Academic Press. Topol, E. (2019). Deep Medicine: How AI Can Make Healthcare Human Again. Hachette UK. |
| Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347–1358. Esteva, A., et al. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24–29. |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications | |||||||
| No | Program Qualification | Contribution Level | |||||
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | PQ-1. Knows how to reach current basic, theoretical and applied scientific knowledge in the field of occupational therapy by using information technologies and resources; evaluates the accuracy, reliability and validity of this information. | X | |||||
| 2 | PQ-2. Uses accurate assessment methods for individuals and communities in terms of activity and participation, plans therapy and applies it within the scope of evidence-based occupational therapy theory and foundations. | X | |||||
| 3 | PQ-3. Describes a person's nature, needs and performance in relation to daily life, production, leisure activities and tasks, and the relationship between activity and health and well-being. | X | |||||
| 4 | PQ-4. Works in a person-centered manner by interpreting activity and participation limitations and using activities in prevention, rehabilitation and treatment. | X | |||||
| 5 | PQ-5. Carries out his/her professional and academic studies effectively and ethically, has the ability to work independently and actively within and between disciplines. | X | |||||
| 6 | PQ-6. Within the framework of social responsibility awareness, determines needs in research, projects and activities related to occupational therapy science, creates relevant research questions, researches independently and continues lifelong learning. | X | |||||
| 7 | PQ-7. Uses information resources effectively by adopting the features of adapting to new conditions, learning, developing new ideas, giving importance to quality throughout life. | X | |||||
| 8 | PQ-8. Determines personal and professional learning needs, learns at least one foreign language, develops a positive attitude towards lifelong learning and demonstrates what she has learned. | X | |||||
| 9 | PQ-10. Expresses herself effectively by using information and communication technologies related to the field of occupational therapy and establishing verbal and written communication. | X | |||||
| 10 | PQ-10. In the development of occupational therapy, acts in accordance with the legal regulations, scientific and professional ethical values that concern his field as an individual; The client fulfills the responsibilities required by his professional performance, protects and defends his professional rights by observing his rights. | X | |||||
Assessment Methods
| Contribution Level | Absolute Evaluation | |
| Rate of Midterm Exam to Success | 40 | |
| Rate of Final Exam to Success | 60 | |
| Total | 100 | |