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About Study Course

Credit points / ECTS:2 / 3
Course supervisor:Maija Radziņa
Study type:Full time
Course level:Master's
Target audience:Medicine; Medical Technologies; Public Health; Rehabilitation; Dentistry
Language:English, Latvian
Branch of science:Clinical Medicine; Roentgenology and Radiology


The study course "Artificial intelligence in imaging" is intended for in-depth understanding of the versatile application of radiology data in clinical medicine using digitization tools.
With the development of technology, the volume of radiology images and data has grown rapidly, increasing the workload of radiologists and requiring more detailed solutions and innovative approaches to solutions for various clinical needs, including the speed of data circulation. In this context, artificial intelligence (AI), which is increasingly integrated into daily practice in today's radiology, offers ample opportunities to improve the diagnostic process of radiology. AI can help prioritize patients with more severe and acute pathologies for faster diagnosis, choose appropriate image acquisition protocols, automate various measurements, image analysis and interpretation, compare current and previous examination images, automate examination description with voice-to-text conversion programs and optimize conclusion standardization, thereby reducing the consumption of resources and the time until the diagnosis is obtained and, therefore, the timely initiation of therapy through a multifaceted approach. This allows radiologists to pay attention to the most complex cases earlier and to facilitate and speed up the diagnostic process, thus improving the quality of patient care. Visual information modeling for individual needs is also needed in stomatology, rehabilitation and traumatology-orthopedics, as well as in other sectors, and AI solutions are becoming more relevant in the evaluation of implants and biomechanics.


Informatics, Anatomy.

Learning outcomes


1. Students should be able to critically evaluate AI claims and understand the connection between models and clinical realities.
2. Students have a robust conceptual understanding of AI and the structure of clinical data science.


1. Students have been involved in hands - on workshops with the focus - recognizing appropriate potential applications of AI to health data.
2. Understanding how to discern between different methods that can be applied to data (e.g. the distinction between prediction and causal inference approaches).


After successfully completing the course, the student will gain experience and be able to use various AI tools in practice.

Study course planning

Planning period:Year 2023, Autumn semester
Study programmeStudy semesterProgram levelStudy course categoryLecturersSchedule
Medicine, MF7Master’sLimited choiceMaija Radziņa, Nauris Zdanovskis, Edgars Edelmers
Medicine, SSNMF7Master’sLimited choiceMaija Radziņa, Edgars Edelmers, Nauris Zdanovskis
Planning period:Year 2024, Spring semester
Study programmeStudy semesterProgram levelStudy course categoryLecturersSchedule
Medicine, MF8Master’sLimited choiceEdgars Edelmers, Maija Radziņa, Nauris Zdanovskis
Medicine, SSNMF8Master’sLimited choiceEdgars Edelmers, Maija Radziņa, Nauris Zdanovskis