Duration
Research methodology : 15h Th
Artificial intelligence tools in dental sciences : 15h Th
Number of credits
| Bachelor in dentistry | 2 crédits |
Lecturer
Research methodology : Isabelle Laleman, Christelle Sanchez, Dorien Van hede
Artificial intelligence tools in dental sciences : Marcia Belleflamme, Giovanni Briganti, Isabelle Laleman, Christelle Sanchez, Dorien Van hede
Language(s) of instruction
French language
Organisation and examination
Teaching in the second semester
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
Artificial intelligence tools in dental sciences
This part of the unit introduces artificial intelligence tools applied to dental sciences: principles of machine and deep learning in dental imaging; current clinical applications (caries and periapical lesion detection on periapical and panoramic radiographs, automated segmentation and cephalometry, implant and orthodontic planning, digital CAD/CAM workflows, mucosal lesion screening support); critical appraisal of validation studies (external validation, reproducibility, selection and spectrum bias); the European regulatory framework (AI Act, medical devices, GDPR); and the ethical and relational implications of introducing these tools into dental practice.
Learning outcomes of the learning unit
Artificial intelligence tools in dental sciences
By the end of this part, students will be able to describe how AI tools used in dentistry work, distinguish clinically validated applications from those still at the research stage, critically appraise the methodology of a diagnostic performance study, situate a tool within the applicable European regulatory framework, and discuss the practitioner's role in AI-assisted decisions along with the limitations of these systems.
Prerequisite knowledge and skills
Artificial intelligence tools in dental sciences
No formal prerequisites. Basic knowledge of dental imaging, descriptive statistics and the research methodology covered in the first part of the unit is drawn upon.
Planned learning activities and teaching methods
Artificial intelligence tools in dental sciences
Ten hours of asynchronous online teaching on the Braintop platform, organised as structured modules combining short theoretical units, readings and self-correcting formative quizzes. Five hours of face-to-face teaching devoted to interactive sessions: critical appraisal of articles, clinical case discussion, guided demonstration of tools, and questions arising from the online modules.
Mode of delivery (face to face, distance learning, hybrid learning)
Artificial intelligence tools in dental sciences
Blended learning
Further information:
Hybrid
Course materials and recommended or required readings
Artificial intelligence tools in dental sciences
Platform(s) used for course materials:
- eCampus
Further information:
La santé à l'ère du numérique. N. Neysen & G. Briganti
Artificial intelligence tools in dental sciences
Exam(s) in session
Any session
- In-person
written exam ( multiple-choice questionnaire )
Further information:
Written multiple-choice examination in the June session, covering all content taught online and face to face. Assessment criteria are the accuracy of theoretical knowledge, understanding of methodological principles, and the ability to transfer these to clinical situations. This mark is combined into the overall mark for the learning unit.
Work placement(s)
Organisational remarks and main changes to the course
Contacts
Artificial intelligence tools in dental sciences
Prof. Giovanni Briganti giovanni.briganti@uliege.be