Digital Technology and Artificial Intelligence in Dental Medicine

Code:

MIMD010214

Acronym:

IAMD

Keywords
Classification	Keyword
OFICIAL	Dental Medicine

Instance: 2025/2026 - 2S

Active?	Yes
Responsible unit:	Gestão Académica
Course/CS Responsible:	Integrated Master in Dental Medicine

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
MIMD	97	Official Plan for the Academic Year 2025/2026	1	-	4	54	108

Teaching language

Portuguese

Objectives

1. Understand the fundamentals of digital technology and artificial intelligence (AI) in the context of oral health and dental medicine.


2. Develop basic digital literacy, including the ability to recognize digital equipment and workflows in clinical practice.


3. Become familiar with key concepts, tools, and applications of digital health technologies such as CAD/CAM, CBCT, intraoral scanners, and planning software.


4. Acquire foundational knowledge of how artificial intelligence works, including types of AI, algorithms, and neural networks, even without prior programming experience.


5. Explore real and current applications of AI in dentistry, such as assisted diagnosis, digital orthodontic planning, clinical triage, and remote monitoring.


6. Intuitively understand the principles of computational thinking, algorithmic logic, and the sequential organization of instructions (pseudocode), relating them to clinical reasoning.


7. Engage with visual educational programming platforms, such as Scratch and Machine Learning for Kids, to intuitively simulate automated clinical decisions.


8. Develop critical and ethical thinking about the risks, limitations, and implications of using AI in healthcare, including algorithmic bias, explainability, and the dehumanization of care.


9. Recognize the importance of privacy and the protection of clinical data in accordance with the GDPR, and understand the ethical responsibilities involved in using AI-based tools.


10. Understand the principles and guidelines of responsible and regulated AI, particularly European Union regulations, and the role of healthcare professionals in their implementation.


11. Apply acquired knowledge in an integrated final project, involving critical analysis of a digital or AI application in dentistry, with the production of a written report and oral presentation.


12. Develop scientific communication, collaboration, and autonomy skills through the preparation and presentation of group work.

Learning outcomes and competences

1. Demonstrate basic digital literacy, including the identification and understanding of key devices, software, and digital workflows applied to clinical practice in dentistry.


2. Explain the fundamental concepts of digital technology and artificial intelligence using accessible and context-appropriate technical vocabulary for oral health.


3. Recognise the role of digital transformation in dentistry, including the evolution of clinical, laboratory, and diagnostic processes.


4. Critically interpret the functioning of artificial intelligence systems, including concepts such as machine learning, artificial neural networks, and predictive algorithms.


5. Identify real and current clinical applications of AI in dentistry, such as image-assisted diagnosis, orthodontic planning, automated triage, and remote monitoring.


6. Relate computational structures (pseudocode, flowcharts, conditional logic) to clinical reasoning, even without the use of formal programming language.


7. Intuitively simulate the functioning of algorithms and automated decisions using visual platforms such as Scratch and Machine Learning for Kids.


8. Critically analyse the advantages and limitations of AI in dentistry, considering accuracy, explainability, algorithmic biases, and clinical applicability.


9. Reflect on the ethical risks of AI, including its impact on patient autonomy, data privacy, decision explainability, and the potential dehumanisation of care.


10. Recognise the legal and regulatory obligations related to the use of AI in healthcare, including GDPR principles, informed consent, and European Union AI regulations.


11. Integrate technical, ethical, and clinical knowledge into practical projects by critically analysing real or simulated applications of digital or AI technologies in dentistry.


12. Interpret outputs generated by AI-based software and compare them with human clinical judgement, fostering an integrated understanding of digital practice.


13. Demonstrate critical thinking in evaluating technological solutions based on criteria such as applicability, benefits, risks, transparency, and ethical impact.


14. Develop computational and logical thinking by breaking down complex problems into structured steps and creating solutions based on digital workflows.


15. Work collaboratively on scientific analysis and communication projects, including the development of structured reports and oral presentations on digital applications in oral health.


16. Apply creativity and autonomy in solving simulated clinical problems using computational reasoning and educational digital tools.


17. Clearly communicate technical and ethical ideas related to digital technology, adapting communication to different audiences (peers, instructors, simulated patients).


18. Actively contribute to building a more ethical, critical, and responsible clinical practice in response to the emerging challenges of AI and digital transformation.


19. Demonstrate initial skills in applied research by consulting, selecting, and analysing technical and scientific sources on AI in dentistry.


20. Formulate well-founded arguments regarding the use (or non-use) of AI in specific clinical situations, based on technical, ethical, legal, and scientific criteria.

Working method

Presencial

Program

Module 1 – Introduction to Digital Technologies in Healthcare

Concept of digital technology: differences between analog and digital, role of data, interoperability, and interfaces.

Digital transformations in medicine and dentistry: technological evolution, task automation, telemedicine.

Digital equipment and workflows in clinical practice: intraoral scanner, CAD/CAM, 3D printing, digital radiology, CBCT.

Module 2 – Fundamentals of Artificial Intelligence

Concept of Artificial Intelligence: AI vs natural intelligence, programmed algorithms vs machine learning.

Types of AI: narrow AI, machine learning, artificial neural networks.

Practical examples of AI in dentistry: caries detection, orthodontic planning, clinical chatbots.

Limitations, biases, and ethical implications of AI.

Module 3 – Artificial Intelligence in Dentistry

Real clinical applications of AI: radiological diagnosis, automatic lesion detection, digital triage.

Software used in practice: Diagnocat, Pearl, DentalMonitoring, 3Shape TRIOS with AI.

Interpretation of AI outputs versus human clinical evaluation.

Critical discussion of risks, benefits, and limitations.

Module 4 – Intuitive Programming Concepts (no code)

Computational thinking: problem decomposition, pattern recognition, abstraction, pseudocode.

Algorithmic logic: conditional structures, flowcharts, logical operators.

Visual programming with Scratch and Machine Learning for Kids: construction of animated clinical workflows and simulations.

Training and testing of simplified AI models.

Module 5 – Ethics, Privacy, and Responsible AI

Informed consent in digital contexts and the use of algorithms.

Principles of clinical data protection and GDPR: anonymization, encryption, risks of data sharing.

AI regulation in healthcare: AI Act, EU ethical guidelines, role of professional bodies.

Ethical dilemmas, algorithmic biases, autonomy, and explainability in clinical decision-making.

Module 6 – Integrated Final Project

Selection and critical analysis of a digital or AI application in dentistry.

Technical, ethical, clinical, and scientific evaluation of the chosen application.

Preparation of a written report and oral presentation.

Development of scientific communication, autonomy, teamwork, and critical thinking skills.

Mandatory literature

T. Shan, F.R. Tay, and L. Gu; Application of Artificial Intelligence in Dentistry, 2021
Julian Savulescu, Alberto Giubilini, Robert Vandersluis, Abhishek Mishra; Ethics of artificial intelligence in medicine, 2024
Irene Dankwa-Mullan; Health Equity and Ethical Considerations in Using Artificial Intelligence in Public Health and Medicine, 2024
American Medical Association; Advancing health care AI through ethics evidence and equity, 2025
European Commission; High-Level Expert Group on Artificial Intelligence. Ethics Guidelines for Trustworthy AI, 2019
Hao Ding, Jiamin Wu, Wuyuan Zhao, Jukka P. Matinlinna, Michael F. Burrow and James K. H. Tsoi; Artificial intelligence in dentistry — A review, 2023
Rabaï Bouderhem; Shaping the future of AI in healthcare through ethics and governance, 2024

Teaching methods and learning activities

Theoretical-Expository Teaching (interactive lectures)

Presentation of content through dynamic lectures supported by slides, short videos, and visual demonstrations.

Guided class discussions on clinical applications and ethical implications of technology and AI.

Use of platforms like Kahoot, Socrative, or Google Forms for interactive review quizzes.

Practical and Laboratory Sessions

Demonstrations of digital equipment (intraoral scanner, CAD/CAM, 3D printing, CBCT) and AI-assisted software (Diagnocat, DentalMonitoring).

Exploration of educational AI tools, such as Teachable Machine, Machine Learning for Kids, and Scratch.

Simulations of automated clinical triage, interpretation of AI-generated outputs, and comparison with human clinical reasoning.

Critical analysis of simulated cases, with group discussions on algorithmic decisions and their implications.

Collaborative mini-projects, such as constructing flowcharts or pseudocode for solving simple clinical problems.

Roleplay Activities and Ethical Debates

Simulations of informed consent in digital contexts.

Discussion of ethical and bioethical dilemmas based on clinical cases involving AI (e.g., bias, explainability, regulation).

Collaborative creation of a mini code of conduct for the responsible use of AI in dentistry.

Integrated Final Project

Selection and critical analysis of a digital or AI application in oral healthcare.

Technical, ethical, clinical, and scientific evaluation of the selected application.

Preparation of a structured mini-report (1000–1500 words).

Oral presentation using slides, videos, or simulations, involving all group members.

Tutorial sessions for project guidance and feedback.

Active and Collaborative Learning

Group work on practical activities and projects.

Individual and group critical reflection.

Autonomous exploration of sources, software, and innovative clinical practices.

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Designation	Weight (%)
Participação presencial	20,00
Teste	20,00
Trabalho prático ou de projeto	60,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Apresentação/discussão de um trabalho científico	8,00
Elaboração de projeto	18,00
Elaboração de relatório/dissertação/tese	10,00
Estudo autónomo	20,00
Frequência das aulas	30,00
Trabalho de investigação	5,00
Trabalho escrito	7,00
Trabalho laboratorial	10,00
Total:	108,00

Eligibility for exams

Course attendance is granted upon participation in at least 75% of practical classes and 90% of practical assessments, regardless of the student’s special status.

Calculation formula of final grade

The Final Grade (FG) for the course unit is determined based on the following components:

FG=(0,20×TP)+(0,20×QZ)+(0,60×PF)

Where:

TP = Tasks and Participation in class (assessed weekly)

QZ = Interactive Quiz (individual assessment on theoretical and practical content)

PF = Final Project (written mini-report + group oral presentation)