Course Description
Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|---|---|---|---|---|
| AI in MEDICINE II | ISM1216131 | Spring Semester | 32+0 | - | 4 |
| Course Program |
| Prerequisites Courses | |
| Recommended Elective Courses |
| Language of Course | English |
| Course Level | First Cycle (Bachelor's Degree) |
| Course Type | Elective |
| Course Coordinator | Prof.Dr. Zübeyir BAYRAKTAROĞLU |
| Name of Lecturer(s) | Prof.Dr. Mehmet Kemal ÖZDEMİR, Prof.Dr. Zübeyir BAYRAKTAROĞLU, Assist.Prof. Emrullah GÜLTEKİN |
| Assistant(s) | |
| Aim | This course provides an in-depth exploration of the applications, challenges, and future directions of Artificial Intelligence (AI) in the field of medicine. Students will learn the fundamentals of AI, machine learning, and data science, and how these technologies are transforming healthcare, from diagnostics to treatment planning and patient care. Students will learn about various AI techniques and their implementation in medical practice and in their medical education. The course will provide applications that will include AI in diagnostics, treatment, patient care, and image/data analysis. The course combines theoretical concepts with practical case studies and hands-on exercises/projects. |
| Course Content | This course contains; Introduction to AI Methods and their Applications in Medicine,Machine Learning Basics,Data Collection and Preprocessing,Supervised Learning,Unsupervised Learning,Model Evaluation and Performance Metrics,Deep Learning in Medicine,Medical Imaging and AI,Natural Language Processing (NLP) in Healthcare ,AI in Diagnostics and Disease Prediction,AI in Personalized Medicine, Treatment Planning, Drug Discovery,AI in Medical Robotics and Genomics,Challenges and Limitations of AI in Medicine, and Future Trends,Course Review and Project Presentations. |
| Course Learning Outcomes | Teaching Methods | Assessment Methods |
| Analyze the role of AI in various medical fields, including diagnostics, imaging, personalized medicine, and drug discovery. | ||
| Understand the fundamental concepts of AI and machine learning. | ||
| Learn and apply AI models to solve specific problems in medicine. | ||
| Gain hands-on experience with AI tools and platforms through practical exercises and projects. | ||
| Stay informed about the latest advancements, research studies, and trends in AI and healthcare. |
| Teaching Methods: | |
| Assessment Methods: |
Course Outline
| Order | Subjects | Preliminary Work |
|---|---|---|
| 1 | Introduction to AI Methods and their Applications in Medicine | Lecture Notes 1 |
| 2 | Machine Learning Basics | Lecture Notes 2 |
| 3 | Data Collection and Preprocessing | Lecture Notes 3 |
| 4 | Supervised Learning | Lecture Notes 4 |
| 5 | Unsupervised Learning | Lecture Notes 5 |
| 6 | Model Evaluation and Performance Metrics | Lecture Notes 6 |
| 7 | Deep Learning in Medicine | Lecture Notes 7 |
| 8 | Medical Imaging and AI | Lecture Notes 8 |
| 9 | Natural Language Processing (NLP) in Healthcare | Lecture Notes 9 |
| 10 | AI in Diagnostics and Disease Prediction | Lecture Notes 10 |
| 11 | AI in Personalized Medicine, Treatment Planning, Drug Discovery | Lecture Notes 11 |
| 12 | AI in Medical Robotics and Genomics | Lecture Notes 12 |
| 13 | Challenges and Limitations of AI in Medicine, and Future Trends | Lecture Notes 13 |
| 14 | Course Review and Project Presentations | Lecture Notes 14 |
| Resources |
| There are no required textbooks for this course. Reference and reading materials will be provided via the course professor via Microsoft Teams. |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications | |||||||
| No | Program Qualification | Contribution Level | |||||
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | PQ1: Knows the morphological and functional normal and abnormal structure of human body. | ||||||
| 2 | PQ2: Knows the essential ways of determining the underlying causes of the pathologies with basic scientific approaches and the diagnoses of illnesses and disorders. | ||||||
| 3 | PQ3: Knows the reasons for illnesses, the ways of protection, and the methods of promotion and improvement of public health. | ||||||
| 4 | PQ4: Knows the methods of advancing his/her knowledge about health and its practice. | X | |||||
| 5 | PQ5: Accesses, interprets and applies the advanced interdisciplinary information related to health. | X | |||||
| 6 | PQ6: Performs a complete clinical examination of the human body, both morphologically and functionally and defines the problems. | ||||||
| 7 | PQ7: Interprets examination data for diagnoses, compares with clinical data, and provides solutions. | ||||||
| 8 | PQ8: Selects and applies appropriate tools for promotion and improvement of individual and public health. | X | |||||
| 9 | PQ9: Plans and conducts an advanced study of health independently. | X | |||||
| 10 | PQ10: Takes responsibility individually and as a team member to solve the problems encountered in the promotion and improvement of individual and public health. | ||||||
| 11 | PQ11: Takes responsibility for any intervention on the human body for the diagnosis and treatment. | ||||||
| 12 | PQ12: Determines personal learning requirements and decides and develops a positive lifelong learning attitude. | X | |||||
| 13 | PQ13: Evaluates the information gained in the field of health with a critical approach. | ||||||
| 14 | PQ14: Informs the patient, the relevant people and institutions, and the public about the health problem and conveys recommendations of solutions in writing and/or verbally. | ||||||
| 15 | PQ15: Shares their recommendations on promotion and improvement of health with interdisciplinary experts by supporting with data. | X | |||||
| 16 | PQ16: Uses English at least at the General Level of European Language Portfolio B1, follows resources in his/her field and communicates. | ||||||
| 17 | PQ17: Uses computer software, information, and communication technologies at least at the Advanced Level of European Computer Operating License. | X | |||||
| 18 | PQ18: Acts in accordance with social, scientific, cultural and ethical values in the stages of obtaining, interpreting, applying and announcing the data related to the field of health. | X | |||||
| 19 | PQ19: Develops strategy, policy and implementation plans on health issues and evaluate the results obtained the framework of quality processes. | ||||||
| 20 | PQ20: Systematically shares his/her works on promoting and improving health with quantitative and qualitative data and interdisciplinary experts. | X | |||||
| 21 | PQ21: Has sufficient awareness on occupational health and safety issues. | ||||||
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 | 3 | 42 | |||
| Guided Problem Solving | 0 | 0 | 0 | |||
| Resolution of Homework Problems and Submission as a Report | 6 | 4 | 24 | |||
| Term Project | 12 | 2 | 24 | |||
| Presentation of Project / Seminar | 0 | 0 | 0 | |||
| Quiz | 0 | 0 | 0 | |||
| Midterm Exam | 1 | 10 | 10 | |||
| General Exam | 1 | 10 | 10 | |||
| Performance Task, Maintenance Plan | 0 | 0 | 0 | |||
| Total Workload(Hour) | 110 | |||||
| Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30*=(110/30) | 4 | |||||
| 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 |
|---|---|---|---|---|---|
| AI in MEDICINE II | ISM1216131 | Spring Semester | 32+0 | - | 4 |
| Course Program |
| Prerequisites Courses | |
| Recommended Elective Courses |
| Language of Course | English |
| Course Level | First Cycle (Bachelor's Degree) |
| Course Type | Elective |
| Course Coordinator | Prof.Dr. Zübeyir BAYRAKTAROĞLU |
| Name of Lecturer(s) | Prof.Dr. Mehmet Kemal ÖZDEMİR, Prof.Dr. Zübeyir BAYRAKTAROĞLU, Assist.Prof. Emrullah GÜLTEKİN |
| Assistant(s) | |
| Aim | This course provides an in-depth exploration of the applications, challenges, and future directions of Artificial Intelligence (AI) in the field of medicine. Students will learn the fundamentals of AI, machine learning, and data science, and how these technologies are transforming healthcare, from diagnostics to treatment planning and patient care. Students will learn about various AI techniques and their implementation in medical practice and in their medical education. The course will provide applications that will include AI in diagnostics, treatment, patient care, and image/data analysis. The course combines theoretical concepts with practical case studies and hands-on exercises/projects. |
| Course Content | This course contains; Introduction to AI Methods and their Applications in Medicine,Machine Learning Basics,Data Collection and Preprocessing,Supervised Learning,Unsupervised Learning,Model Evaluation and Performance Metrics,Deep Learning in Medicine,Medical Imaging and AI,Natural Language Processing (NLP) in Healthcare ,AI in Diagnostics and Disease Prediction,AI in Personalized Medicine, Treatment Planning, Drug Discovery,AI in Medical Robotics and Genomics,Challenges and Limitations of AI in Medicine, and Future Trends,Course Review and Project Presentations. |
| Course Learning Outcomes | Teaching Methods | Assessment Methods |
| Analyze the role of AI in various medical fields, including diagnostics, imaging, personalized medicine, and drug discovery. | ||
| Understand the fundamental concepts of AI and machine learning. | ||
| Learn and apply AI models to solve specific problems in medicine. | ||
| Gain hands-on experience with AI tools and platforms through practical exercises and projects. | ||
| Stay informed about the latest advancements, research studies, and trends in AI and healthcare. |
| Teaching Methods: | |
| Assessment Methods: |
Course Outline
| Order | Subjects | Preliminary Work |
|---|---|---|
| 1 | Introduction to AI Methods and their Applications in Medicine | Lecture Notes 1 |
| 2 | Machine Learning Basics | Lecture Notes 2 |
| 3 | Data Collection and Preprocessing | Lecture Notes 3 |
| 4 | Supervised Learning | Lecture Notes 4 |
| 5 | Unsupervised Learning | Lecture Notes 5 |
| 6 | Model Evaluation and Performance Metrics | Lecture Notes 6 |
| 7 | Deep Learning in Medicine | Lecture Notes 7 |
| 8 | Medical Imaging and AI | Lecture Notes 8 |
| 9 | Natural Language Processing (NLP) in Healthcare | Lecture Notes 9 |
| 10 | AI in Diagnostics and Disease Prediction | Lecture Notes 10 |
| 11 | AI in Personalized Medicine, Treatment Planning, Drug Discovery | Lecture Notes 11 |
| 12 | AI in Medical Robotics and Genomics | Lecture Notes 12 |
| 13 | Challenges and Limitations of AI in Medicine, and Future Trends | Lecture Notes 13 |
| 14 | Course Review and Project Presentations | Lecture Notes 14 |
| Resources |
| There are no required textbooks for this course. Reference and reading materials will be provided via the course professor via Microsoft Teams. |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications | |||||||
| No | Program Qualification | Contribution Level | |||||
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | PQ1: Knows the morphological and functional normal and abnormal structure of human body. | ||||||
| 2 | PQ2: Knows the essential ways of determining the underlying causes of the pathologies with basic scientific approaches and the diagnoses of illnesses and disorders. | ||||||
| 3 | PQ3: Knows the reasons for illnesses, the ways of protection, and the methods of promotion and improvement of public health. | ||||||
| 4 | PQ4: Knows the methods of advancing his/her knowledge about health and its practice. | X | |||||
| 5 | PQ5: Accesses, interprets and applies the advanced interdisciplinary information related to health. | X | |||||
| 6 | PQ6: Performs a complete clinical examination of the human body, both morphologically and functionally and defines the problems. | ||||||
| 7 | PQ7: Interprets examination data for diagnoses, compares with clinical data, and provides solutions. | ||||||
| 8 | PQ8: Selects and applies appropriate tools for promotion and improvement of individual and public health. | X | |||||
| 9 | PQ9: Plans and conducts an advanced study of health independently. | X | |||||
| 10 | PQ10: Takes responsibility individually and as a team member to solve the problems encountered in the promotion and improvement of individual and public health. | ||||||
| 11 | PQ11: Takes responsibility for any intervention on the human body for the diagnosis and treatment. | ||||||
| 12 | PQ12: Determines personal learning requirements and decides and develops a positive lifelong learning attitude. | X | |||||
| 13 | PQ13: Evaluates the information gained in the field of health with a critical approach. | ||||||
| 14 | PQ14: Informs the patient, the relevant people and institutions, and the public about the health problem and conveys recommendations of solutions in writing and/or verbally. | ||||||
| 15 | PQ15: Shares their recommendations on promotion and improvement of health with interdisciplinary experts by supporting with data. | X | |||||
| 16 | PQ16: Uses English at least at the General Level of European Language Portfolio B1, follows resources in his/her field and communicates. | ||||||
| 17 | PQ17: Uses computer software, information, and communication technologies at least at the Advanced Level of European Computer Operating License. | X | |||||
| 18 | PQ18: Acts in accordance with social, scientific, cultural and ethical values in the stages of obtaining, interpreting, applying and announcing the data related to the field of health. | X | |||||
| 19 | PQ19: Develops strategy, policy and implementation plans on health issues and evaluate the results obtained the framework of quality processes. | ||||||
| 20 | PQ20: Systematically shares his/her works on promoting and improving health with quantitative and qualitative data and interdisciplinary experts. | X | |||||
| 21 | PQ21: Has sufficient awareness on occupational health and safety issues. | ||||||
Assessment Methods
| Contribution Level | Absolute Evaluation | |
| Rate of Midterm Exam to Success | 40 | |
| Rate of Final Exam to Success | 60 | |
| Total | 100 | |