Artificial Intelligence in Health Care

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Credits
3
Types
Elective
Requirements
This subject has not requirements, but it has got previous capacities
Department
URV;CS
Web
https://moodle.urv.cat
Heath care (HC) is one of the main application domains of artificial intelligence (AI) since its appearance in 1956. Most of the AI technologies find a natural application area in HC problems though the benefit of this application has been sometimes called into question. In the last times, however, we've witnessed a revival of the interest of AI applied to medicine.

By means of the analysis of remarkable published articles, during this course the student will be introduced in several AI solutions to HC needs and problems and will correlate the AI concepts and technologies studied in other subjects of the master in the resolution (or support to the resolution) of HC problems.

Classes are every other week.

Teachers

Person in charge

  • Domenec Savi Puig Valls ( )

Weekly hours

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

Competences

Generic Technical Competences

Generic

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

Technical Competences of each Specialization

Academic

  • CEA8 - Capability to research in new techniques, methodologies, architectures, services or systems in the area of ??Artificial Intelligence.

Professional

  • 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

Teamwork

  • 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.

Reasoning

  • CT6 - Capability to evaluate and analyze on a reasoned and critical way about situations, projects, proposals, reports and scientific-technical surveys. Capability to argue the reasons that explain or justify such situations, proposals, etc..

Basic

  • CB7 - Ability to integrate knowledges 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 their 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

  1. Capacity to read, understand, and relate the information contained in scientific & technological documents
    Related competences: CB7, CB8, CB9, CT3, CT6, CEA8, CG1,
  2. Train the synthesis, preparation, exposition, and defense of scientific topics in public
    Related competences: CB7, CB8, CB9, CT3, CT6, CEA8, CG1,
  3. Ability to connect and complement own ideas with other's and also with AI technologies explained in other courses
    Related competences: CT3, CT6, CEP3, CEP6,

Contents

  1. Artificial intelligence in health care
    A review of the state of AI in health care will be analyzed
  2. Grand challenges in clinical decision support
    A review of the pending reseach and development CDS open problems will be analyzed
  3. Data mining in health care
    A review of important AI data mining technologies and their application to medicine will be analyzed
  4. Big data analytics in health care
    A description of BDA and its application to health care will be analyzed
  5. IBM Watson
    The use of IBM Watson and technology underneath when applied to health care will be analyzed
  6. Ethical challenges and recommendations in AIHC
    Ethical framework of AI when applied to medicine

Activities

Activity Evaluation act


Introduction of the course

The professor will expose the relevant issues related to the subject: Content; Material; Calendar; Evaluation; Bibliography
  • Theory: Presentation of the professor and course, evaluation method and dynamics of classes

Theory
2h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Preparation of 5 topics by the students

The five topics of the subject are prepared by the students in groups, every other week.
  • Autonomous learning: The student will develop 5 topics for presentation in groups
Objectives: 1
Contents:
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
40h

Exposition & questions 1

Students expose and answer questions about topic 1 (in group).
Objectives: 2
Week: 3
Theory
3h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
2h

Exposition & questions 2

Students expose and answer questions about topic 2 (in group).
Objectives: 2
Week: 5
Theory
3h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
2h

Exposition & questions 3

Students expose and answer questions about topic 3 (in group).
Objectives: 2
Week: 7
Theory
3h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
2h

Exposition & questions 4

Students expose and answer questions about topic 4 (in group).

Week: 9
Theory
3h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
2h

Exposition & questions 5

Students expose and answer questions about topic 5 (in group).

Week: 11
Theory
3h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
2h

Conclusions I by the professor

The conclusions of the course are exposed.
Objectives: 3
Theory
3h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

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 his response to the questions. After the presentation of all groups, there will be an open discussion among all groups about topic. This methodology will be repeated five times throughout the course, each with a different topic of IA applied to medicine.

Evaluation methodology

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

Bibliography

Basic:

Complementary:

  • Thirty years of artificial intelligence in medicine (AIME) conferences: A review of research themes - Peek, N.; Combi, C.; Marín, R.; Bellazi, R., Artificial intelligence in medicine , .
    https://pubmed.ncbi.nlm.nih.gov/26265491/
  • Artificial Intelligence transforms the future of health care - Noorbakhsh-Sabet, N.; Zand, R,; Zhang, Y.; Abedi, V., American journal of medicine , .
    https://pubmed.ncbi.nlm.nih.gov/30710543/

Web links

Previous capacities

Basic concepts of AI.