| Dozent/in |
Javier Montoya Dr. sc. ETH |
| Veranstaltungsart |
Vorlesung |
| Code |
HS261110 |
| Semester |
Herbstsemester 2026 |
| Durchführender Fachbereich |
Gesundheitswissenschaften |
| Studienstufe |
Bachelor |
| Termin/e |
Do, 17.09.2026, 14:15 - 16:00 Uhr, 4.B47
Do, 24.09.2026, 14:15 - 16:00 Uhr, 4.B47
Do, 01.10.2026, 14:15 - 16:00 Uhr, 4.B47
Do, 08.10.2026, 14:15 - 16:00 Uhr, 4.B47
Do, 15.10.2026, 14:15 - 16:00 Uhr, 4.B47
Do, 22.10.2026, 14:15 - 16:00 Uhr, 4.B47
Do, 29.10.2026, 14:15 - 16:00 Uhr, 4.B47
Do, 05.11.2026, 14:15 - 16:00 Uhr, 4.B47
Do, 12.11.2026, 14:15 - 16:00 Uhr, 4.B47
Do, 19.11.2026, 14:15 - 16:00 Uhr, 4.B47
Do, 26.11.2026, 14:15 - 16:00 Uhr, 4.B47
Do, 03.12.2026, 14:15 - 16:00 Uhr, 4.B47
Do, 10.12.2026, 14:15 - 16:00 Uhr, 4.B47
Do, 17.12.2026, 14:15 - 16:00 Uhr, 4.B47
Fr, 22.01.2027, 13:00 - 14:15 Uhr, HS 9 (Prüfung) |
| Weitere Daten |
Weekly sessions over one semester (2 hours per week).
Detailed schedule and organizational information are provided via Moodle. |
| Umfang |
2 Semesterwochenstunden |
| Inhalt |
* Introduction to Artificial Intelligence and its role in healthcare
* AI development pipeline (data collection, preprocessing, training, evaluation)
* Fundamentals of machine learning (supervised, unsupervised, self-supervised, and reinforcement learning)
* Core machine learning methods (e.g., linear models, ensemble methods) with focus on intuition and application)
* Model evaluation and performance metrics
* Applications in digital health
* Introduction to deep learning concepts and architectures
* AI in clinical decision support and real-world healthcare applications
* Trustworthy and explainable AI
* Ethical and regulatory aspects
* Limitations, risks, and challenges of AI systems
* Current developments and future perspectives |
| Schlagworte |
Nachhaltigkeit |
| E-Learning |
The Moodle platform is used for the provision of teaching materials, communication, and exam preparation. |
| Lernziele |
After successful completion of the course, students are able to:
* explain fundamental concepts of Artificial Intelligence and Machine Learning
* describe how AI systems are developed, trained, and evaluated using data
* distinguish between common AI approached (supervised, unsupervised, self-supervised, and reinforcement learning)
* interpret selected applications of AI in medical imaging and clinical text analysis
* assess the potential, limitations, and risks of AI systems in healthcare contexts
* outline key ethical and regulatory considerations related to AI in healthcare
* communicate AI-related concepts to both technical and non-technical audiences |
| Voraussetzungen |
The course provides a structured and self-contained introduction to Artificial Intelligence, with a focus on applications in healthcare.
It is designed for students from different academic backgrounds.
No prior knowledge of Artificial Intelligence or programming is required.
Basic digital literacy and active participation are expected. |
| Sprache |
Englisch |
| Anmeldung |
Moodle: https://elearning.hsm-unilu.ch/course/view.php?id=1077
|
| Leistungsnachweis |
* 70% written exam (multiple choice and short-answer questions, open notes)
* 30% group presentation (case-based analysis)
The written exam assesses the understanding of fundamental concepts and the ability to interpret and evaluate AI applications.
The group presentation is conducted in small groups (2–3 students) and focuses on the structured analysis of an application of Artificial Intelligence in healthcare. The group assignment is designed as a lightweight task and does not require technical implementation.
Assessment criteria for the group presentation include clarity of explanation, conceptual understanding, critical reflection, and structured argumentation.
IMPORTANT: In order to earn credits and participate at the exam registration via Uni Portal within the exam registration period is MANDATORY. Further information: www.unilu.ch/en/study/courses-exams-regulations/health-sciences-and-medicine/exams/ |
| Abschlussform / Credits |
Oral exam / 3 Credits
|
| Hinweise |
This course provides an introductory overview of Artificial Intelligence with a focus on applications in healthcare.
The course does not require programming and emphasizes conceptual understanding, interpretation, and critical assessment of AI systems.
Selected topics are illustrated through case-based discussions and real-world healthcare applications. |
| Hörer-/innen |
Nein |
| Kontakt |
javier.montoya@doz.unilu.ch |
| Material |
Teaching materials include lecture slides, selected scientific publications, book chapters, and online resources.
Case studies and example applications are used to illustrate real-world use of Artificial Intelligence in healthcare. |
| Literatur |
Reading materials are provided via Moodle and include introductory resources on Artificial Intelligence and Machine Learning, as well as selected scientific publications in digital health and medical AI.
Additional optional readings are provided for students interested in further technical depth. |