TÜRKÇE
ENGLISH
Information
| Code | VTS321 |
| Name | Artificial Intelligence in Veterinary Medicine |
| Term | 2024-2025 Academic Year |
| Semester | 5. Semester |
| Duration (T+A) | 1-0 (T-A) (17 Week) |
| ECTS | 1 ECTS |
| National Credit | 1 National Credit |
| Teaching Language | Türkçe |
| Level | Lisans Dersi |
| Type | Normal |
| Label | GC General Culture Courses E Elective |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Dr. Öğr. Üyesi SİNAN KANDIR |
| Course Instructor |
Dr. Öğr. Üyesi SİNAN KANDIR
(A Group)
(Ins. in Charge)
|
Course Goal / Objective
This course aims to introduce students to the fundamental concepts of artificial intelligence and teach its applications in veterinary medicine. The course covers the use of AI in veterinary medicine, including decision support systems, imaging technologies, animal health management, and big data analysis.
Course Content
This course covers the fundamental concepts and principles of artificial intelligence, machine learning and deep learning models, and the application of AI in veterinary medicine for disease diagnosis, treatment planning, and animal health management. Additionally, it explores decision support systems, fuzzy logic applications, big data analysis, and the use of the Internet of Things (IoT) in farm management. Topics such as veterinary radiology, image analysis, artificial neural networks, data security, and ethical considerations are discussed to evaluate the current and future potential of AI applications in veterinary medicine.
Course Precondition
N/A
Resources
- An Introduction to Veterinary Medicine Engineering, Springer Cham, 2023 - Artificial Intelligence: A Modern Approach – Stuart Russell & Peter Norvig (4. Ed., 2021)
Notes
Relevant literature, video and web-based materials, lecture notes, presentations.
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Explains fundamental concepts and algorithms of artificial intelligence. |
| LO02 | Identifies artificial intelligence applications used in veterinary medicine. |
| LO03 | Describes the role of AI and machine learning models in veterinary diagnosis and treatment. |
| LO04 | Understands the use of digitalization and Internet of Things (IoT) technologies in veterinary medicine. |
| LO05 | Evaluates the application of artificial neural networks and deep learning models in veterinary medicine. |
| LO06 | Understands the importance of big data analysis in veterinary medicine and its integration with AI. |
| LO07 | Discusses the ethical aspects and data security issues related to artificial intelligence applications. |
| LO08 | Follows recent developments in artificial intelligence applications in veterinary medicine. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Distinguishes animal species and breeds based on their structural (morphological), functional (physiological, biochemical) and behavioral characteristics and whether they can be consumed for food purposes. | |
| PLO02 | Bilgi - Kuramsal, Olgusal | Explains the sources, physical and chemical properties and preparation of drugs. | |
| PLO03 | Bilgi - Kuramsal, Olgusal | Describes the effects and side effects of medications on patients | |
| PLO04 | Bilgi - Kuramsal, Olgusal | Explains the formation mechanisms of animal diseases and their pathological effects on the body. | |
| PLO05 | Bilgi - Kuramsal, Olgusal | It makes efficiency evaluations among animal breeds and, when necessary, carries out selection, hybridization and artificial insemination at a level that can carry out breeding work. | |
| PLO06 | Bilgi - Kuramsal, Olgusal | Disposes of dead animal material, tissue samples, medical and chemical waste appropriately in accordance with international, national laws and regulations. | 3 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | It determines the correct approach area for the patient by evaluating the examination, operation etc. practices in the clinic according to the topographic anatomy. | |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Uses biochemical parameters in diagnostic processes by evaluating the information obtained with cause-effect relationships. | |
| PLO09 | Beceriler - Bilişsel, Uygulamalı | Writes prescriptions in accordance with diagnosis and treatment protocols for diseases and poisoning in animals. | |
| PLO10 | Beceriler - Bilişsel, Uygulamalı | Implements necessary measures, including biosecurity, to control infectious diseases. | 4 |
| PLO11 | Beceriler - Bilişsel, Uygulamalı | Performs necropsy to evaluate findings in dead animals, directs tissues to the necessary laboratory according to appropriate storage methods and prepares a report. | |
| PLO12 | Beceriler - Bilişsel, Uygulamalı | Monitors patients in the post-operative process according to the main treatment methods of surgical diseases in animal species. | |
| PLO13 | Beceriler - Bilişsel, Uygulamalı | Applies sedation, anesthesia and pain management methods safely according to animal species. | |
| PLO14 | Beceriler - Bilişsel, Uygulamalı | Correctly applies biosafety principles such as sterilization of clinical and laboratory equipment and clothing. | 3 |
| PLO15 | Beceriler - Bilişsel, Uygulamalı | It regulates the care and feeding protocols according to animal species and supervises the consumption of animal foods produced for human consumption or foods containing animal products in their composition. | 5 |
| PLO16 | Beceriler - Bilişsel, Uygulamalı | Evaluates laboratory results of animal diseases and applies correct diagnosis, prognosis and treatment methods based on the data obtained. | 5 |
| PLO17 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | He/she is conscious of performing his/her profession by being aware of his/her powers and responsibilities. | |
| PLO18 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Examines scientific techniques and methods according to facts related to veterinary services and takes analysis or methodological responsibility for problems. | 2 |
| PLO19 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | It uses adequate technology in the hygienic production, manufacturing, processing, consumption and sales of food by conducting health control of animal food products produced for human consumption. | |
| PLO20 | Yetkinlikler - Öğrenme Yetkinliği | Lists the methods of producing and using scientific knowledge in his/her professional field. | 5 |
| PLO21 | Yetkinlikler - Öğrenme Yetkinliği | Uses updated legislation regarding veterinary services when necessary. | 2 |
| PLO22 | Yetkinlikler - Öğrenme Yetkinliği | Performs an examination appropriate to the case, applying the contribution of imaging methods to diagnosis and radiation safety in accordance with current regulations. | 1 |
| PLO23 | Yetkinlikler - Öğrenme Yetkinliği | Keeps clinical and patient records using proper and scientific methods. | |
| PLO24 | Yetkinlikler - Öğrenme Yetkinliği | By observing the basic principles of medicine, it develops management methods and appropriate management plans for animal production enterprises. | |
| PLO25 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Implement social projects and plans with social awareness, based on professional skills and authority with a computer usage license, advanced computer software. | |
| PLO26 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Works in cooperation with other colleagues by following the world agenda within the framework of professional ethics and deontology rules. | |
| PLO27 | Yetkinlikler - Alana Özgü Yetkinlik | Produces solutions to field-specific problems in line with scientific data/evidence. | |
| PLO28 | Yetkinlikler - Alana Özgü Yetkinlik | He/she has the ethical values of the veterinary profession and defends these values. | |
| PLO29 | Yetkinlikler - Alana Özgü Yetkinlik | Takes appropriate precautions by clinically recognizing zoonosis and notifiable diseases. | |
| PLO30 | Yetkinlikler - Alana Özgü Yetkinlik | Determines the methods of diagnosis, prevention, treatment and operation of diseases in different animal species. | |
| PLO31 | Yetkinlikler - Alana Özgü Yetkinlik | In emergency cases, intervenes in all animals and applies first aid methods. | |
| PLO32 | Yetkinlikler - Alana Özgü Yetkinlik | Makes judgments on animal husbandry principles and breeding by assessing the physical condition, welfare and rearing conditions of domestic animals, communicating appropriately with animal owners and colleagues. | |
| PLO33 | Yetkinlikler - Alana Özgü Yetkinlik | Independently interprets articles and presentations published in other scientific fields related to Veterinary Medicine to maintain professional development and skills. | |
| PLO34 | Yetkinlikler - Alana Özgü Yetkinlik | When necessary, euthanizes animals using appropriate methods and respecting the feelings of animal owners. | |
| PLO35 | Yetkinlikler - Alana Özgü Yetkinlik | By mastering the concepts of occupational safety in terms of the workplace and the personnel working with it, they have the competence to apply and defend it. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to Artificial Intelligence | Reviewing related literature and lecture notes | Öğretim Yöntemleri: Anlatım, Beyin Fırtınası, Soru-Cevap |
| 2 | Fundamentals of AI Algorithms | Reading about AI algorithms | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 3 | Machine Learning and Deep Learning | Reviewing machine learning models | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Beyin Fırtınası |
| 4 | Decision Support Systems and Fuzzy Logic | Reading about decision support systems in veterinary medicine | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Beyin Fırtınası |
| 5 | AI Programming Languages and Applications | Basic exploration of Python, R | Öğretim Yöntemleri: Anlatım, Gösterip Yaptırma, Grup Çalışması |
| 6 | Genetic Algorithms in Veterinary Medicine | Researching genetic algorithms in biomedical applications | Öğretim Yöntemleri: Anlatım, Gösterip Yaptırma, Soru-Cevap, Beyin Fırtınası |
| 7 | AI-Based Imaging Systems | Reviewing veterinary imaging systems | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Gösterip Yaptırma, Beyin Fırtınası |
| 8 | Mid-Term Exam | Reviewing previous topics | Ölçme Yöntemleri: Yazılı Sınav, Ödev, Proje / Tasarım, Performans Değerlendirmesi, Sözlü Sınav |
| 9 | Artificial Neural Networks: Perception and Feedback Mechanisms | Principles of artificial neural networks | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Problem Çözme, Proje Temelli Öğrenme |
| 10 | Artificial Neural Networks: Deep Learning Models | Reading about deep learning models | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Gösterip Yaptırma, Grup Çalışması |
| 11 | Machine Learning and Prediction Models | Researching machine learning algorithms | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Gösterip Yaptırma, Grup Çalışması |
| 12 | Big Data Analysis and Data Mining | Reviewing big data applications in veterinary medicine | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Gösterip Yaptırma, Grup Çalışması, Beyin Fırtınası |
| 13 | IoT Technologies in Livestock Farming | Literature review on IoT applications | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Problem Çözme, Proje Temelli Öğrenme , Beyin Fırtınası, Grup Çalışması |
| 14 | AI Applications in Veterinary Medicine: Case Studies | Literature review on case studies | Öğretim Yöntemleri: Soru-Cevap, Tartışma, Proje Temelli Öğrenme , Örnek Olay, Beyin Fırtınası, Grup Çalışması |
| 15 | AI Ethics and Data Security | Reading about AI ethics and data security | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Problem Çözme, Beyin Fırtınası, Tartışma |
| 16 | Term Exams | Reviewing previous topics | Ölçme Yöntemleri: Yazılı Sınav, Sözlü Sınav, Ödev, Proje / Tasarım, Performans Değerlendirmesi |
| 17 | Term Exams | Reviewing previous topics | Ölçme Yöntemleri: Yazılı Sınav, Sözlü Sınav, Ödev, Proje / Tasarım, Performans Değerlendirmesi |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 1 | 14 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 1 | 14 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 1 | 2 | 2 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 2 | 2 |
| Final Exam | 1 | 2 | 2 |
| Total Workload (Hour) | 34 | ||
| Total Workload / 25 (h) | 1,36 | ||
| ECTS | 1 ECTS | ||