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Academic year
Didactic period
Secondo Semestre

Training objectives

The aim of the course is to make the student aware of the potential offered by Artificial Intelligence tools for the treatment of knowledge and data in the medical field. As future doctor, be aware of the potential offered by artificial intelligence technologies when applied to medicine.


Attendance of a basic course on computer science applications and tools.

Course programme

The course is organized in five modules, 1 CFU each. Contents are:

Module 1 - 1 CFU
Introduction to Artificial Intelligence
First Order Logic as representation language
Rule-based systems and Decision Support Systems (DSS)
Applications: case study DSS for clinical exams
Practical exercises for rule formalization

Module 2 - 1 CFU
Constraint problems
Constraint representation and search techniques in solution space
Applications in the medical-health field, examples

Module 3 - 1 CFU
Unsupervised learning
Clustering, association rules
Data mining systems
Medical data applications (with open source databases)
Exercises with the Weka framework

Module 4 - 1 CFU
Automatic Machine Learning systems
Supervised Learning systems
Neural Networks (fundaments), with Computer Vision applications
Applications in the medical-health field, examples
Practical exercises using the framework Weka

Module 5 - 1 CFU
What is an ontology: basic concepts
Ontologies in Medicine
Inference systems for ontologies
Practical exercises

Didactic methods

Teaching activity is carried out partly with lessons and practical exercises, and partly via the Moodle platform.

Learning assessment procedures

The final exam tests the degree of achievement of the training objectives indicated above.
Students are partly evaluated through the evaluation by the teacher of the assigned exercises, and through a final written exam (consisting of five open questions, one for each module) about the topics presented during the lessons.
The overall grade is obtained as an average weighted by the average grade in the exercises (which weighs 30% on the final grade), and by the grade in the written part (which weighs 70% on the final grade)

Reference texts

Slides, and papers provided by teachers of each module.