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.
Person in charge
David Riaño (
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 in 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.
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 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..
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.
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
A review of the state of AI in health care will be analyzed
Grand challenges in clinical decision support
A review of the pending reseach and development CDS open problems will be analyzed
Data mining in health care
A review of important AI data mining technologies and their application to medicine will be analyzed
Big data analytics in health care
A description of BDA and its application to health care will be analyzed
The use of IBM Watson and technology underneath when applied to health care will be analyzed
Conclusions to AI in health care
Summary of important issues of AI in health care
Introduction of the course
The professor will expose the relevant issues related to the subject: Content; Material; Calendar; Evaluation; Bibliography
Preparation of 5 topics by the students
The five topics of the subject are prepared by the students in groups, every other week. Objectives:1 Contents:
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.
Presentations (60%): adjustment to time; clarity of presentation (oral); clarity of presentation (slides); addressed all the relevant issues in the questions; amenity will be the main issues considered for the evaluation of the presentations.
Participation in discussions of other's presentations (40%)
The coming of age of artificial intelligence in medicine. -
Patel VL, Shortliffe EH, et al. ,
J AIIM 46, 5-17., 2009.
Grand Challenges in clinical decision support. -
Sitting DF, Wrigth A, et al. ,
JBI 41, 387-392, 2008.
A survey on data mining approaches for healthcare, -
Tomar D, Agarwal S.,
IJ Bio-sci & Bio-tech. 5(5), 241-266., 2013.
Big data analytics in healthcare: promise and potential. -
Raghupathi W, Raghupathi V.,
Health info science and syst. 2:3, 2014.
IBM Watson: how cognitive computing can be applied to big data challenges in life sciences research. -
Chen Y, Argentinis E, et al.,
Clinical therapeutics 38(4), 688-701, 2016.
Basic concepts of AI.
Where we are
B6 Building Campus Nord
C/Jordi Girona Salgado,1-3
08034 BARCELONA Spain
Tel: (+34) 93 401 70 00