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Education

Integrating AI in Biomedical, Pharmaceutical, and Health Care Research (EDUC Programme)

Data

Official data in SubjectManager for the following academic year: 2024-2025

Course director

Number of hours/semester

lectures: 0 hours

practices: 0 hours

seminars: 14 hours

total of: 14 hours

Subject data

  • Code of subject: OXF-IAI-g-T
  • 1 kredit
  • Pharmacy
  • Optional modul
  • autumn
Prerequisites:

-

Course headcount limitations

min. 5 – max. 24

Available as Campus course for . Campus-karok: ETK GYTK TTK

Topic

Students with background on computer engineering and pharmaceutics/biotechnology will collaborate on a project using AI on data related to biomedical or health technology. As technology revolutionizes healthcare, understanding the principles and applications of AI is crucial for advancing drug discovery, personalized medicine, and therapeutic development. The project is part of two courses running at University of Pécs (PTE) and University of South-Eastern Norway (USN).

Lectures

Practices

Seminars

  • 1.

    Recreuitment: Gathering Campus-credit students at PTE for EDUC course

    - Kvell Krisztián, Pál Szilárd
  • 2.

    Introduction: Meeting and information for all students interested in COIL

    - Kvell Krisztián, Pál Szilárd
  • 3.

    Start of project: Defining project, group members and project scope

    - Kvell Krisztián, Pál Szilárd
  • 4.

    Implementation I: Collect data

    - Kvell Krisztián, Pál Szilárd
  • 5.

    Implementation II: Develop working model

    - Kvell Krisztián, Pál Szilárd
  • 6.

    Implementation III: Finalize working model

    - Kvell Krisztián, Pál Szilárd
  • 7.

    Testing: Test the model and consider ethical dilemmas

    - Kvell Krisztián, Pál Szilárd
  • 8.

    Presentation: Present project online

    - Kvell Krisztián, Pál Szilárd
  • 9.

    Conclusion: Discussion and concluding remarks

    - Kvell Krisztián, Pál Szilárd
  • 10.

    Take home messages: Final discussion with Campus-credit students of PTE

    - Kvell Krisztián, Pál Szilárd
  • 11.

    Az EDUC kurzus tapasztalatainak összegyűjtése

    - Kvell Krisztián, Pál Szilárd
  • 12.

    Az EDUC kurzus tapasztalatainak elemzése

    - Kvell Krisztián, Pál Szilárd
  • 13.

    Az EDUC kurzus továbbfejlesztése

    - Kvell Krisztián, Pál Szilárd
  • 14.

    Az EDUC kurzus konklúzióinak egyeztetése hazai és nemzetközi, szakmai és adminisztratív partnereinkkel

    - Kvell Krisztián, Pál Szilárd

Reading material

Obligatory literature

Literature developed by the Department

Notes

Recommended literature

Explainable Artificial Intelligence for Biomedical Applications (River Publishers, ISBN: 978-8770228497, by Utku Kose, Deepak Gupta and Xi Chen)

Conditions for acceptance of the semester

Successful project work and group presentation.

Mid-term exams

Written test on basics then project work.

Making up for missed classes

Project work is not time or space limited, maybe done at home, anytime.

Exam topics/questions

Test questions will focus on basics, but project work presentation is decisive.

Examiners

  • Kvell Krisztián
  • Pál Szilárd

Instructor / tutor of practices and seminars

  • Kvell Krisztián
  • Pál Szilárd
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