Artificial Intelligence in Health Science

Course Information
TitleΤεχνητή Νοημοσύνη στις Επιστήμες Υγείας / Artificial Intelligence in Health Science
CodeΙΑ2062
FacultyHealth Sciences
SchoolMedicine
Cycle / Level1st / Undergraduate
Teaching PeriodWinter
CoordinatorIoanna Chouvarda
CommonNo
StatusActive
Course ID600020387

Programme of Study: UPS of School of Medicine (2019-today)

Registered students: 32
OrientationAttendance TypeSemesterYearECTS
KORMOSElective Courses532

Class Information
Academic Year2021 – 2022
Class PeriodWinter
Faculty Instructors
Instructors from Other Categories
Weekly Hours2
Class ID
600187924
SectionInstructors
1. Εργαστήριο Η/Υ, Ιατρικής Πληροφορικής & Βιοϊατρική
Course Type 2021
Specific Foundation
Course Type 2011-2015
General Foundation
Mode of Delivery
  • Face to face
  • Distance learning
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
  • Greek (Instruction, Examination)
Prerequisites
Required Courses
  • ΙΑ1008 Medical Informatics
General Prerequisites
Introduction to medical informatics. Familiar with Computer applications. If possible, familiar with matlab or R.
Learning Outcomes
Aim of the course. Artificial intelligence and data-driven research is a growing area of ​​medical research, and is expected to be part of the medical practice in the coming years. The aims of the course "Artificial Intelligence (AI) in Health Sciences" include understanding the use of machine learning and deep learning methods in medicine, and familiarization with utilization in various directions to solve specific problems. Information is presented about applications that utilize medical image, biological data, everyday data, etc. Aims. With the lectures, the demonstration of technologies and applications, the laboratory exercises and group projects, the students are given the opportunity to: • Understand the concepts and theory around artificial intelligence issues. • Understand the necessary terminology and importance on issues related to AI. • Understand the basic methods of analysis and machine learning in problems based on biomedical data • Understand the role and importance of AI in problems of medical practice. • Be familiar with the use of analysis / AI tools in problems applying to medical practice. • Be familiar with computer-based tools in medical procedures and problems. • To utilize according to their needs the AI technologies in medical research and education.
General Competences
  • Apply knowledge in practice
  • Retrieve, analyse and synthesise data and information, with the use of necessary technologies
  • Work autonomously
  • Work in teams
  • Work in an interdisciplinary team
  • Advance free, creative and causative thinking
Course Content (Syllabus)
1 Introductory course on AI and Machine Learning 2 Machine Learning - Theory 3 Machine Learning Lab - hands-on 4 Presentation of examples of interest in Medicine - with the participation of students 5 Deep learning - Theory 6 Deep learning - laboratory 7 AI and Computing infrastructure at AUTh 8 Ethics and thrustworthiness of AI, explainability / interpretability - - Interaction of AI and human 9 AI & Medical Decision Support 10 Medical Image, Radiomics & TN in diagnosis and prognosis 11 Machine Learning Applications and Biological Data 12 AI applications with Clinical data and Biosignals 13 AI in the management of the patient's daily life 14 Student work and discussion
Keywords
Computational / Artificial Intelligence
Educational Material Types
  • Slide presentations
  • Video lectures
  • Interactive excersises
  • Book
Use of Information and Communication Technologies
Use of ICT
  • Use of ICT in Course Teaching
  • Use of ICT in Laboratory Teaching
  • Use of ICT in Communication with Students
  • Use of ICT in Student Assessment
Description
The nature of the course makes the use of Information and Communication technologies necessary in all steps. All exercises take place on PC. Electronic submission of assignments / tests is used.
Course Organization
ActivitiesWorkloadECTSIndividualTeamworkErasmus
Lectures190.7
Laboratory Work90.3
Reading Assigment50.2
Project120.4
Exams110.4
Total562
Student Assessment
Description
Lab exercises / Course exercises / Assignment 1 / Assignment 2 - project / Written Examination with Multiple Choice Questions
Student Assessment methods
  • Written Exam with Multiple Choice Questions (Summative)
  • Written Assignment (Summative)
  • Labortatory Assignment (Formative, Summative)
Bibliography
Course Bibliography (Eudoxus)
"ΑΙΚΑΤΕΡΙΝΗ ΓΕΩΡΓΟΥΛΗ, ΤΕΧΝΗΤΗ ΝΟΗΜΟΣΥΝΗ, 2016, Ηλεκτρονικό Βιβλίο, ISBN 978-960-603-031-4, Ελληνικά Ακαδημαϊκά Ηλεκτρονικά Συγγράμματα και Βοηθήματα - Αποθετήριο ""Κάλλιπος""https://repository.kallipos.gr/handle/11419/3381" ΚΩΝΣΤΑΝΤΙΝΟΣ ΔΙΑΜΑΝΤΑΡΑΣ, ΔΗΜΗΤΡΗΣ ΜΠΟΤΣΗΣ, ΜΗΧΑΝΙΚΗ ΜΑΘΗΣΗ, 2019, ISBN 978-960-461-995-5, ΕΚΔΟΣΕΙΣ ΚΛΕΙΔΑΡΙΘΜΟΣ ΕΠΕ Βερύκιος, Β., Καγκλής, Β., Σταυρόπουλος, Η., Η επιστήμη των δεδομένων μέσα από τη γλώσσα R. 2015. ηλεκτρ. βιβλ.] Αθήνα: Σύνδεσμος Ελληνικών Ακαδημαϊκών Βιβλιοθηκών. Διαθέσιμο στο: http://hdl.handle.net/11419/2965 https://repository.kallipos.gr/handle/11419/2965
Additional bibliography for study
Θα δοθούν επιστημονικά άρθρα και αλλα βιβλια
Last Update
15-09-2021