Medical Imaging Physics & Acquisition (ECTS 6)

Mathematics for Medical Artificial
Intelligence (ECTS 6)

Introduction to Robotics for Medical AI (ECTS 6)

Digital Image Processing for Medical
Applications (ECTS 5)

Programming for Medical Imaging (ECTS 5)

Scientific and Critical Thinking in
Medical Research (ECTS 2)

Machine Learning for Medical Data (ECTS 6)

Deep Learning for Medical Imaging (ECTS 6)

Robotics for Medical Applications (ECTS 6)

Medical Image Segmentation and
Analysis (ECTS 5)

Regulation, Ethics and Trustworthy AI in Healthcare (ECTS 5)

Innovation, Translation and Teamwork
in Medical AI (ECTS 2)

Advanced Methods in Medical Imaging
and AI (ECTS 6)

Multimodal and Longitudinal Medical
Imaging Analysis (ECTS 6)

Self-Supervised and Foundation
Models in Medical Imaging (ECTS 6)

Image-Guided Medical Interventions (ECTS 5)

Evaluation and Validation of Medical AI Systems (ECTS 5)

Regulatory and Professional Context in Medical AI (ECTS 2)

MSC Thesis in a company or any of the partners institutions UdG, uB or UNICLAM (ECTS 24)

Scientific Writing and Publishing (ECTS 2)

Secure Deployment of Medical AI
Systems (ECTS 2)

Career Development and PhD
Preparation (ECTS 2)

The curriculum is structured around three complementary pillars: (i) medical image acquisition, analysis and image-guided interaction with the physical world, (ii) AI-driven modelling, learning and perception-action techniques, and (iii) clinical validation, regulatory and ethical aspects of medical image analysis and intervention. These pillars are addressed in a transversal manner and progressively reinforced across all semesters, with increasing depth, integration, and practical relevance as students advance through the programme. Together, this structure ensures that graduates acquire both advanced technical expertise and the transversal competences required for the responsible design, validation and deployment of medical AI systems in real-world healthcare settings

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MAIA programme has been designed to ensure a clear, coherent, and progressive educational pathway, fully integrated across the three consortium universities. Students follow a mandatory cohort-based mobility scheme, moving together from one institution to the next each semester. During the first semester at UBE, students acquire solid foundations in signal and image processing, machine learning, and medical imaging. In the second semester, they move to the UniCas, where they deepen their knowledge in advanced computer vision and machine and deep learning methods. The third semester takes place at the UdG, focusing on applied, hands-on modules in computer-aided diagnosis and medical applications. Finally, during the fourth semester, students carry out their master’s thesis at one of the consortium universities or at an associated partner institution, including research centres, universities or companies.

In addition to these modules, a Winter School is organised every February in Cassino, serving as a key joint academic milestone of MAIA. The Winter School brings together students from both the first and second year across the consortium, with differentiated participation modes reflecting their respective stages of academic progression. First-year students attend on site, while second-year students participate online, as they are already engaged in their Master’s thesis activities. Beyond its teaching function, the Winter School acts as a shared European learning space that strengthens programme integration across partner institutions. It serves as the formal launch of the online transversal modules of the fourth semester.

An induction week is organised each year in September during which the enrolled students are informed about the programme, the assessment and the rules. The induction week also permits new students to meet MAIA graduate students.