Artificial Intelligence in Health Care

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

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, CEP6, CEP3,

    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
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Exposition & questions 2

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

    Exposition & questions 3

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

    Exposition & questions 4

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

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

    Exposition & questions 5

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

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

    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.