ECTS credits ECTS credits: 3
ECTS Hours Rules/Memories Hours of tutorials: 1 Expository Class: 10 Interactive Classroom: 15 Total: 26
Use languages Spanish, Galician
Type: Ordinary Degree Subject RD 1393/2007 - 822/2021
Departments: Electronics and Computing
Areas: Computer Science and Artificial Intelligence
Center Higher Technical Engineering School
Call: Second Semester
Teaching: With teaching
Enrolment: Enrollable
Health is one of the areas in which artificial intelligence traditionally has the greatest application due to the complexity of decision making, which benefits greatly from the ability of AI techniques to handle complex problems.
The aim of this subject is to focus on the study and application of machine learning techniques, computer vision, language technologies, and other knowledge acquired by the student in previous courses, to address complex health problems. The complexity and particularities of health data will also be addressed among those who are in the presence of noise, incomplete data, or have no explicit knowledge.
This is a subject of synthesis and exhibition of the different aspects of the application of AI in Health both through research projects funded in public calls and in projects with companies and institutions, which will be able to prepare students for their professional future both in the field of research and in the field of the company or public administration.
Theme 1. General perspective of AI applied to health. Design and planning of research projects. Initiative and Entrepreneurship. Specific aspects of health AI law.
Theme 2. Data spaces in health.
Theme 3. R&D&I project in health: presentation of ongoing research projects funded by the State Research Agency and the Carlos III Health Institute; preparation of projects from idea to memory: challenges, strengths, and weaknesses. Formation of multidisciplinary teams. Obtaining data. Distribution of tasks. Challenges in opening new lines of work.
Theme 4. Health applications in general clinical practice in Galicia.
There will not be a bibliography as such, but it will work with scientific articles and presentations of experiences in research and transfer projects.
The subject contributes to the development of the general skills listed in the degree report:
General Skills:
[GS3] Ability to design and create quality models and solutions based on artificial intelligence that are efficient, robust, transparent, and responsible.
[GS4] Ability to select and justify the appropriate methods and techniques to solve a specific problem, or to develop and propose new methods based on artificial intelligence.
Learning results:
- Know and know how to treat the specific characteristics of the data in the health environment.
- Know the organization of an AI project in health- Know the main machine learning techniques applied to the health field.
- Know and know how to apply computer vision techniques to medical images.
- Know and know how to apply language models in health.
The following methodology uses the USC Virtual Campus as a basic platform.
In the virtual classroom of the subject, the student will have all the information and documentation of the subject.
The subject will be delivered in 10 hours of exhibition sessions and 15 hours of interactive sessions.
In these sessions, there will be a presentation and discussion of different aspects of the research projects in which the professor of the subject participates.
The CG3 and CG4 competences have associated contents in the theoretical-practical part of the matter and are explicitly evaluated in the tests carried out throughout the course.
TR3 competition is primarily concerned with the promotion of creativity, critical thinking, evaluating the different possibilities of problem solving, both in theory and practice, proposing problems to develop these skills, with changing situations, and evaluating the quality of the work delivered.
TR6 competition is evaluated, including the legal, social, environmental, and economic aspects of the solutions proposed to the different exercises, as well as the explanation of the exercises during the development of the exhibition classes.
The development of the subject will be carried out with a 1h exhibition and 1.5 h of weekly interactive sessions, although there may be small changes during the course of the teaching, holidays, etc.
The first week of class will be the presentation of the subject by the coordinator of the same.
Then, in the second week of class, the theme 1 will be developed, a general theme of introduction to AI in the field of health and how to build a research project, including aspects of initiative and entrepreneurship.
There will also be talk of specific aspects of health AI law, and there will be a brief discussion on different aspects of it.
In the third week of class, the topic of Health Data Spaces will be developed.
From the fourth week of class, presentations and discussions of different types of projects will be held with companies, or projects funded in the health strategy or in AI, as well as aspects to be contemplated in the formation of interdisciplinary teams or the opening of new lines of research. This topic will be developed for 6 weeks.
In the last week, there will be health AI applications that are currently in use in the Galician health system.
The sessions will end with the presentation of works done by the students, and a discussion of them.
As it is a matter of the last semester of the fourth year, and the student is already carrying out his TFG and external practices, the evaluation will consist of the development of a synthesis work on the different aspects of health AI presented at the different sessions of the subject.
Thus, the preparation, presentation, and discussion of said work will be 80% of the final qualification, and there will be a final test that will count 20% of the final qualification, as set out in the title memory.
Given the case of the subject, attendance at class is mandatory, with a minimum of 80% to be able to surpass the subject.
- 80% of the final qualification: Synthesis work on the different aspects of health AI seen during the course (a scheme of the same will be provided on the virtual campus).
In this percentage will be valued the memory, presentation, and participation during the classes.
- 20% of the final qualification: Final test of reflection on what has been learned in the subject.
It is used to evaluate CG3 and CG4 competences.
The final score of the subject will be calculated with the above weights provided that a final score of 5 points is reached and a minimum rating of each part (work, final qualification) of 4 points.
If the 5 points are not reached, the qualification will be the minimum value of the 2 parts.
The delivery of the work after 1 November will be associated with the consideration of presentation in the qualification of the subject, regardless of attendance at the final exam.
Recuperation (2nd chance):
Students who do not exceed any of the mandatory parts in the continuous evaluation may present themselves for this exam.
Work (80% of the final note): Perform a new work indicated by the teachers of the subject.
Final test (20% of the final note): Reflection exercise on the learning gained in the subject as a whole, with specific questions on each topic.
In the event of fraudulent performance of exercises or tests, the provisions of the Regulations on the assessment of students’ academic performance and the review of grades and subsequent modifications will apply.
In application of the ETSE regulations on plagiarism (approved by Xunta de Escola on 01/03/2012), the total or partial copying of any practical or theory exercise will result in failure in both opportunities of the course, with a grade of 0.0 in both cases.
This subject has 3 ECTS credits, corresponding to a total workload of 75 hours. This time can be broken down into the following sections:
ON-SITE WORK (IN THE CLASSROOM):
* Lectures: 10 hours
* Problem- or case-based learning in small groups: 15 hours
* Tuition: 1 hour
Total classroom time: 26 hours
STUDENT'S PERSONAL WORK:
* Autonomous study: 5 hours
* Writing exercises, papers, etc.: 30 hours
* Evaluation of work, projects, exams: 14 hours
Total personal work time: 49 hours
Being located at the end of the studies, general knowledge of all the techniques studied during the development of the same, the different techniques of automatic learning, the development of integrative projects and business projects, as well as the fields of computer vision and language technologies.
It is recommended to use the tutorial hours to clarify any doubts that may arise in the development of the subject.
The USC Virtual Campus will be used for all teaching, publishing of the material, practice scripts, and delivery of work.
The operating system to use in practice sessions can be either Windows or Linux.
The subject will be taught in Spanish and Galician.
Alberto Jose Bugarin Diz
- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- Phone
- 881816440
- alberto.bugarin.diz [at] usc.es
- Category
- Professor: University Professor
María José Carreira Nouche
Coordinador/a- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- Phone
- 881816431
- mariajose.carreira [at] usc.es
- Category
- Professor: University Professor
Senén Barro Ameneiro
- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- Phone
- 881816469
- senen.barro [at] usc.es
- Category
- Professor: University Professor
Manuel Lama Penin
- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- Phone
- 881816427
- manuel.lama [at] usc.es
- Category
- Professor: University Professor
María Jesús Taboada Iglesias
- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- Phone
- 881813561
- maria.taboada [at] usc.es
- Category
- Professor: University Professor
David Chaves Fraga
- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- Phone
- 881815525
- david.chaves [at] usc.es
- Category
- Professor: LOU (Organic Law for Universities) PhD Assistant Professor
Beatriz Blanco Besteiro
- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- beatriz.blanco [at] usc.es
- Category
- Professor: Intern Assistant LOSU
Marcos Fernandez Pichel
- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- marcosfernandez.pichel [at] usc.es
- Category
- Professor: Intern Assistant LOSU
Nicolas Vila Blanco
- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- Phone
- 881815509
- nicolas.vila [at] usc.es
- Category
- Professor: LOU (Organic Law for Universities) PhD Assistant Professor
Marta Nuñez Garcia
- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- martanunez.garcia [at] usc.es
- Category
- Researcher: Ramón y Cajal
Constanza De La O Andion Garcia
- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- constanza.andion.garcia [at] usc.es
- Category
- Ministry Pre-doctoral Contract
| Wednesday | |||
|---|---|---|---|
| 17:30-18:30 | Grupo /CLE_01 | Spanish, Galician | IA.01 |
| 18:30-20:00 | Grupo /CLIL_01 | Galician, Spanish | IA.01 |
| 05.21.2026 16:00-20:00 | Grupo /CLIL_01 | IA.01 |
| 05.21.2026 16:00-20:00 | Grupo /CLE_01 | IA.01 |
| 06.29.2026 16:00-20:30 | Grupo /CLIL_01 | IA.01 |
| 06.29.2026 16:00-20:30 | Grupo /CLE_01 | IA.01 |