AI in Health

The main objective of this subject is to learn about the different applications of Artificial Intelligence in the field of health, from the initial process of obtaining basic medical data, the integration and exploitation of this data and its final use in diagnoses and specific actions in this field, such as those derived from personalized medicine. The different applications of Artificial Intelligence will be studied, from a global and integral point of view, through the description and study of different cases of success in the application of techniques, tools, or artificial intelligence systems in this field of health. Students will also be trained in the use of specific techniques for the integration of data from heterogeneous sources and the use of the different existing standards.

Learning outcomes:
- To develop solid skills to create complex models that allow personalized diagnoses and prediction of clinical trends, based on heterogeneous sources.
- To know the different standards for data processing in the healthcare field and develop the ability to integrate them in AI projects. To learn the techniques for integrating AI in medical devices.
- Develop the skills to design e-health web applications based on AI models.
- Know the specificities of the fields of application of intelligent monitoring of data and signals in e-health and their real-time restrictions.
- Understand and analyze the technical specificities and models for the reliable and secure transmission, collection, tracing, and processing of data in these contexts.

Integration of data from heterogeneous sources and health standards. Security and privacy of clinical data. Successful cases of application of AI techniques in healthcare. Treatment and diagnosis by medical imaging. E-health and personalised medicine.

There will not be a bibliography as such but will work with scientific articles and talks on the dissemination of experiences.

Basic and general skills
BS6 - Possess and understand knowledge that provides a basis or opportunity for originality in the development and/or application of ideas, often in a research context.
BS7 - Students can apply their acquired knowledge and problem-solving skills in new or unfamiliar environments within broader (or multidisciplinary) contexts related to their field of study.
BS9 - Students can communicate their conclusions and the knowledge and rationale underpinning them to specialist and non-specialist audiences in a clear and unambiguous way.
BS10 - That students possess the learning skills that will enable them to continue studying in a largely self-directed or autonomous manner.
GS1. Maintain and extend grounded theoretical approaches to enable the introduction and exploitation of new and advanced technologies in the field of Artificial Intelligence.
GS2. Successfully tackle all stages of an Artificial Intelligence project.
GS4 - Elaborate adequately and with some originality written compositions or motivated arguments, write plans, work projects, scientific articles and formulate reasonable hypotheses in the field.
GS5 - Work in teams, especially multidisciplinary teams, and be skilled in the management of time, people, and decision-making.

Specific skills
SS4 - Knowledge of the fundamentals and basic techniques of artificial intelligence and their practical application.
SS7 - Ability to understand the implications of the development of an explainable and interpretable intelligent system.
SS19 - Knowledge of different fields of application of AI-based technologies and their capacity to offer a differentiating added value.
SS20 - Ability to deal with interdisciplinary environments and to combine and adapt different techniques, extrapolating knowledge between different fields.
SS21 - Knowledge of the techniques that facilitate the organization and management of AI projects in real environments, the management of resources and the planning of tasks in an efficient manner, considering concepts of knowledge dissemination and open science.
SS22 - Knowledge of techniques that facilitate the security of data, applications and communications and their implications in different fields of application of AI.
SS30 - Being able to pose, model and solve problems that require the application of artificial intelligence methods, techniques, and technologies.

Cross-curricular skills
CS5 - Understand the importance of entrepreneurial culture and know the means available to entrepreneurs.
CS8 - Value the importance of research, innovation, and technological development in the socio-economic and cultural progress of society.
CS9 - Can manage time and resources: develop plans, prioritize activities, identify critical ones, establish deadlines, and meet them.

The methodology used uses the Virtual Campus of the three universities as the basic platform. In the virtual classroom of the subject, students will have all the information (theoretical material, class slides, practice scripts, etc.)
*Lectures: oral presentation (USC/UDC/UVIGO) (broadcast to all students). They mainly develop the skills CB6, CB10, CG4, CG5, CE4, CE7, CE19, CE21, CE22, CT5, and CT8.
*Laboratory sessions (USC/UDC/UVIGO) (broadcast to all students). Presentation of use cases. These mainly develop the competences CB7, CB9, CG1, CG2 and CE20, CE30, and CT9.

The evaluation of learning will be carried out by evaluating the practical works (20% of the final grade), which will be completed with the continuous monitoring of the students' work and the carrying out of objective tests (80%).
Continuous monitoring and objective tests: they are used to assess BS6, BS10, GS4, GS5, SS4, SS7, SS19, SS21, SS22, CS5, and CS8 skills mainly.
Practical cases: they are used to evaluate the BS7, BS9, GS1, GS2, SS20, SS30, and SS9 skills mainly.
In the case of fraudulent performance of exercises or tests, the provisions of the Regulations for academic performance of students and review of qualifications will apply.
In application of the ETSE regulations on plagiarism (approved by the ETSE Council on 12/19/2019), the total or partial copy of some practice or theory exercise will mean failure on both occasions of the course, with a grade of 0.0 in both cases.

At the second opportunity, the continuous assessment (20%) will be completed with a final test (80%).

This subject has 3 ECTS credits, corresponding to a total workload of 75h (presence of 7h/credit). This time can be broken down into the following sections:

PRESENTIAL WORK IN CLASSROOM:
* Master classes: 10 hours
* Laboratory practices: 5 hours
* Problem-based learning, seminars, case studies and projects: 6 hours
Total hours of classroom work in the classroom: 21 hours

PERSONAL WORK OF THE STUDENTS:
* Autonomous study: 10 hours
* Laboratory practices: 15 hours
* Problem-based learning, seminars, case studies and projects: 29 hours
Total: 54 hours

It is recommended to have completed the basic subjects of previous modules. It is recommended to bring the subject up to date and to use tuition sessions to clarify doubts and advise on its development.

The teaching of this subject will be in English.
The expository teaching (10 hours) will be given between the USC, the UDC and the UVIGO and will be broadcast for all students.
The interactive teaching (11 hours) will be given between the USC, the UDC and the UVIGO and will be broadcast for all students.