Artificial Intelligence in Hea - Polytechnic Uni

Tuition Fee

Start Date

Medium of studying

Duration

Details

Program Details

Degree

Masters

Major

Biomedical Engineering | Health Informatics | Artificial Intelligence

Area of study

Information and Communication Technologies | Health

Course Language

English

About Program

Program Overview

University Programs

The university offers a range of programs for students, including bachelor's degrees, master's degrees, and integrated bachelor-master degrees.

Bachelor's Degrees

Bachelor Degree in Informatics Engineering
- Enrolment: Available places
- Curriculum:
  - Syllabus
  - Reassessment
  - Specializations
  - Competences
  - Competences for degree subjects
- Faculty
- Bachelor's Thesis
- Timetables
- Exams
- Academic Regulations
Bachelor Degree in Data Science and Engineering
- Enrolment: Available places
- Curriculum:
  - Syllabus
  - Competences
  - Competences for degree subjects
- Faculty
- Timetables
- Exams
- Academic Regulations and organization
Bachelor Degree in Artificial Intelligence
- Enrolment: Places lliures
- Curriculum:
  - Competences
  - Syllabus
  - Competences for degree subjects
- Faculty
- Timetables
- Exams
- Academic regulations
- Bachelor's thesis
Bachelor Degree in Bioinformatics
- Enrolment: Available places
- Curriculum:
  - Learning Outcomes
  - Syllabus
- Faculty
- Timetables
- Exams
- Academic Regulations
Integrated Bachelor Master Degree
- Enrolment
- Curriculum

Master's Degrees

Master in Informatics Engineering
- Enrolment: Available places
- Curriculum:
  - Syllabus
  - Competences
  - Competences for degree subjects
- Faculty
- Academic Regulations
- Master's Thesis
- Timetables
- Exams
Master in Informatics Engineering - Industrial Modality
- Curriculum
Master in Innovation and Research in Informatics
- Enrolment: Available places
- Curriculum:
  - Syllabus
  - Specializations
  - Competences
  - Competences for degree subjects
- Faculty
- Academic Regulations
- Master's Thesis
- Seminars
- Timetables
- Exams
Master in Artificial Intelligence
- Enrolment: Available places
- Curriculum:
  - Syllabus
  - Competences
  - Competences for degree subjects
- Faculty
- Academic Regulations
- Master's Thesis
- Timetables
- Exams
- FAQs
Master in Cybersecurity
Master in Data Science
- Enrolment: Available places
- Curriculum:
  - Syllabus
  - Competences
  - Competences for degree subjects
- Faculty
- Academic Regulations
- Timetables
- Exams
- Master's Thesis
  - Gender Competency
Erasmus Mundus Master in Big Data Management and Analytics
- Timetables
- Curriculum:
  - Syllabus
- Exams
Master in Urban Mobility
- Curriculum
EUMaster4HPC
- Curriculum
Other Masters
- Master in Pure and Applied Logic
- Master in Computational Modelling in Physics, Chemistry and Biochemistry

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Double degrees

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- CERN (Conseil Européen pour le Recherche Nucléaire)
- Erasmus+
- Latin America
- National Institute of Informatics (NII) Tokyo
- SICUE
- UNITECH
- USA grant programs
- Vulcanus
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inLab FIB

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Rector Gabriel Ferraté Library

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Master in Artificial Intelligence: Artificial Intelligence in Health Care

Description

Artificial intelligence (AI) has been a key application domain in health care since its inception in 1956. This course introduces students to AI solutions for health care needs and problems, correlating AI concepts and technologies with the resolution of health care problems.

Credits

Type

Elective

Requirements

This subject has no requirements but has previous capacities.

Department

URV;CS

Weekly Hours

Theory: 1.5
Problems: 0
Laboratory: 0
Guided learning: 0
Autonomous learning: 3.5

Competences

Generic Technical Competences

CG1: Capability to plan, design, and implement products, processes, services, and facilities in all areas of Artificial Intelligence.

Technical Competences of each Specialization

CEA8: Capability to research new techniques, methodologies, architectures, services, or systems in the area of Artificial Intelligence.
CEP3: Capacity for applying Artificial Intelligence techniques in technological and industrial environments to improve quality and productivity.
CEP6: Capability to assimilate and integrate the changing economic, social, and technological environment to the objectives and procedures of informatic work in intelligent systems.

Transversal Competences

CT3: Ability to work as a member of an interdisciplinary team, as a normal member or performing direction tasks, in order to develop projects with pragmatism and sense of responsibility, making commitments taking into account the available resources.
CT6: Capability to evaluate and analyze situations, projects, proposals, reports, and scientific-technical surveys on a reasoned and critical way.
CB7: Ability to integrate knowledge and handle the complexity of making judgments based on information which, being incomplete or limited, includes considerations on social and ethical responsibilities linked to the application of their knowledge and judgments.
CB8: Capability to communicate conclusions, and the knowledge and rationale underpinning these, to both skilled and unskilled public in a clear and unambiguous way.
CB9: Possession of the learning skills that enable the students to continue studying in a way that will be mainly self-directed or autonomous.

Objectives

Capacity to read, understand, and relate the information contained in scientific & technological documents.
Train the synthesis, preparation, exposition, and defense of scientific topics in public.
Ability to connect and complement own ideas with other's and also with AI technologies explained in other courses.

Artificial intelligence in health care
Grand challenges in clinical decision support
Data mining in health care
Big data analytics in health care
IBM Watson
Ethical challenges and recommendations in AIHC

Activities

Introduction of the course
Preparation of 5 topics by the students
Exposition & questions 1-5
Conclusions I by the professor

Teaching Methodology

The entire course will be worked in groups. A topic of AI applied to health care will be presented to all the groups, an article and a list of questions related to the topic presented will be released. Each group will have two weeks to prepare an oral presentation of 15 minutes which will outline the important issues of the article and their response to the questions.

Evaluation Methodology

Presentations (60%)
Participation in discussions of other's presentations (40%)

Bibliography

Basic

The coming of age of artificial intelligence in medicine
Grand challenges in clinical decision support
A survey on data mining approaches to healthcare
Big data analytics in healthcare: promise and potential
IBM Watson: how cognitive computing can be applied to big data challenges in life sciences research

Complementary

Thirty years of artificial intelligence in medicine (AIME) conferences: A review of research themes
Artificial Intelligence transforms the future of health care

Previous Capacities

Basic concepts of AI.

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Artificial Intelligence in Health Care