Veidlapa Nr. M-3 (8)
Study Course Description
Artificial Intelligence Solutions in Healthcare
Course Code
SVI_003
Branch of Science
Mathematics
ECTS
5.00
Target Audience
Business Management; Health Management; Information and Communication Science; Management Science; Medical Services; Medical Technologies; Political Science; Public Health
LQF
Level 7
Study Type And Form
Full-Time
Study Course Implementer
Course Supervisor
Oskars Radziņš
Structure Unit Manager
Daiga Behmane
Structural Unit
Health Management Teaching Group
Contacts
Riga, Kronvalda boulevard 9, +371 67338307
About Study Course
Objective
To introduce students to the different types of artificial intelligence systems (machine learning algorithms and deep neural networks) and their usage in healthcare. Other goals include: 1) Understanding the importance of data in the artificial intelligence system life-cycle, starting from the the creation of the training dataset up to practical applications; 2) The most common problems associated with system training and their mitigation; 3) Ethical creation and use of artificial intelligence systems; 4) Near and far future perspectives and areas of development. At the end of the course students will be able to navigate through the terminology regarding artificial intelligence, will be capable to create an adequate training data set for an artificial intelligence system, will be capable of evaluating the results provided by an artificial intelligence system and will be able to identify the ethical issues associated with the creation and implementation of artificial intelligence systems.
Preliminary Knowledge
Experience in statistics and programming would be considered advantageous.
Learning Outcomes
Knowledge
1.- Know different types of artificial intelligence systems. - Recognize commonalities and differences between classical machine learning and neural network models. - Know the importance of the data set used for training in the development of an artificial intelligence system. - Know the different ways artificial intelligence can be implemented in healthcare. - Recognize the ethical and legal challenges related to the development and implementation of artificial intelligence.
Skills
1.- Evaluate the adequacy of the artificial intelligence system's training data set for the intended purpose. - Know the choice of an artificial intelligence system, according to the purpose and the available data set. - Identify potential applications of artificial intelligence systems in healthcare. - Identify legal challenges related to the use of artificial intelligence systems in healthcare.
Competences
1.- Manage and adapt the use of the dataset for training the artificial intelligence system. - Create simple models of artificial intelligence systems. - Know how to critically evaluate the results created by artificial intelligence systems. - Create an artificial intelligence system development and survey plan.
Assessment
Individual work
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Title
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% from total grade
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Grade
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1.
Individual work |
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-
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1) Learning the materials available in e-studies (video lectures, articles, publications).
2) Execution of self-tests.
3) Independent work development: define a problem in the healthcare industry that can be solved using artificial intelligence and create a plan for data collection and system development, piloting, implementation and survey.
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Examination
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Title
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% from total grade
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Grade
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1.
Examination |
-
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-
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Evaluation of the study course consists of 3 elements:
1) Final exam test, which includes questions from all course topics - 50%;
2) Development and submission of independent work - 30%;
3) Self-test results - 20%.
A final grade of at least 4 out of 10 points is required for successful completion of the course.
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Study Course Theme Plan
FULL-TIME
Part 1
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Lecture
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Modality
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Location
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Contact hours
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On site
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Auditorium
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2
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Topics
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Introduction to artificial intelligence
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Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Auditorium
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2
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Topics
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Introduction to artificial intelligence
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Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Computer room
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2
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Topics
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Introduction to artificial intelligence
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Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Computer room
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2
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Topics
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Introduction to artificial intelligence
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Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Auditorium
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2
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Topics
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Machine learning systems
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Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Auditorium
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2
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Topics
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Machine learning systems
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Class/Seminar
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Modality
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Location
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Contact hours
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On site
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Computer room
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2
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Topics
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Machine learning systems
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Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Computer room
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2
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Topics
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Machine learning systems
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Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Auditorium
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2
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Topics
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Neural Networks
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Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Auditorium
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2
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Topics
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Neural Networks
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Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Computer room
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2
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Topics
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Neural Networks
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Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Computer room
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2
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Topics
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Neural Networks
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Lecture
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Modality
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Location
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Contact hours
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On site
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Auditorium
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2
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Topics
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Artificial intelligence in image processing
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Lecture
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Modality
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Location
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Contact hours
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On site
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Auditorium
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2
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Topics
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Artificial intelligence in image processing
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Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Computer room
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2
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Topics
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Artificial intelligence in image processing
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Lecture
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Modality
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Location
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Contact hours
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On site
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Auditorium
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2
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Topics
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Artificial intelligence in medical devices
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Lecture
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Modality
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Location
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Contact hours
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On site
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Auditorium
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2
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Topics
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Artificial intelligence in pharmacy
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Lecture
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Modality
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Location
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Contact hours
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On site
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Auditorium
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2
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Topics
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Artificial intelligence and the patient
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Lecture
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Modality
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Location
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Contact hours
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On site
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Auditorium
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2
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Topics
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Ethical considerations regarding artificial intelligence
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Total ECTS (Creditpoints):
5.00
Contact hours:
38 Academic Hours
Final Examination:
Exam
Bibliography
Required Reading
1.
Panesar, A. (2021). Machine learning and AI for healthcare: Big data for improved health outcomes. Apress.
2.
Lloyd, N., Khuman, A.S. (2022). AI in Healthcare: Malignant or Benign?. In: Chen, T., Carter, J., Mahmud, M., Khuman, A.S. (eds) Artificial Intelligence in Healthcare. Brain Informatics and Health. Springer, Singapore.
3.
Coravos A, Chen I., Gordhandas A., Stern A. D. 14.02.2019. We should treat algorithms like prescription drugs, Quartz.
4.
Food and Drug Administration. (2021). Good Machine Learning Practice for Medical Device Development: Guiding Principles.
Additional Reading
1.
Josef C. 01.02.2019. Does the “Artificial Intelligence Clinician” learn optimal treatment strategies for sepsis in intensive care?
2.
Eldakak, A., Alremeithi, A., Dahiyat, E. et al. (2024). Civil liability for the actions of autonomous AI in healthcare: an invitation to further contemplation. Humanit Soc Sci Commun 11, 305
3.
Mittal, S., Thakral, K., Singh, R. et al. (2024). On responsible machine learning datasets emphasizing fairness, privacy and regulatory norms with examples in biometrics and healthcare. Nat Mach Intell 6, 936–949.
4.
Bouderhem, R. (2024). Shaping the future of AI in healthcare through ethics and governance. Humanit Soc Sci Commun 11, 416
5.
Rotmensch, M., Halpern, Y., Tlimat, A. et al. (2017). Learning a Health Knowledge Graph from Electronic Medical Records. Sci Rep 7, 5994
6.
Food and Drug Administration. (2024). Transparency for Machine Learning-Enabled Medical Devices: Guiding Principles.
Other Information Sources
1.
Wang, D., Khosla, A., Gargeya, R., Irshad, H., & Beck, A. H. (2016). Deep learning for identifying metastatic breast cancer. arXiv preprint arXiv:1606.05718.
2.
World Health Organization. (2024). Ethics and governance of artificial intelligence for health. Guidance on large multi-modal models. Licence: CC BY-NC-SA 3.0 IGO.
3.
EU AI Act