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Master’s in Digital Health, Imaging & Clinical Analytics Engineering

Online master's programme of 1,900 hours in digital health, imaging and clinical analytics engineering. 12 modules covering the digital health ecosystem, medical imaging physics and processing, clinical data infrastructure, descriptive and predictive analytics, AI in imaging, clinical language processing, clinical decision support and regulation, with an assigned technical tutor.

Duration1900 h
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Analyse the data ecosystem of digital health.

02

Understand the physics and quality of medical imaging modalities.

03

Process and segment medical images.

04

Build interoperable clinical data infrastructures.

05

Develop validated predictive and AI models.

06

Design clinical decision support systems.

07

Apply regulation and ethics to digital health projects.

Profile

Who it's for

  • Medical imaging engineers working with segmentation, reconstruction or imaging AI.
  • Clinical data scientists building predictive models with hospital data.
  • Digital health analysts designing indicators and dashboards.
  • Healthcare AI engineers bringing models into clinical workflows.
  • Clinical information systems managers.
  • Healthcare digital transformation consultants.
  • Engineering or science graduates seeking to enter hospitals and health services.
Standards & tools

What you'll work with

  • Regulation (EU) 2017/745 (EU Medical Device Regulation, MDR)
  • Regulation (EU) 2024/1689 (EU Artificial Intelligence Act)
  • Regulation (EU) 2016/679 (GDPR, data protection)
  • Regulation (EU) 2025/327 (European Health Data Space)
  • DICOM PS3 (medical imaging format and communication)
  • HL7 FHIR R4 (health data interoperability)
  • Royal Decree 1029/2022 (Spanish regulation on protection against ionising radiation)
  • Law 14/2007 (Spanish Biomedical Research Law)
  • 3D Slicer
  • Python with PyTorch and MONAI
  • Power BI
  • OHDSI ATLAS
  • R
Syllabus

12 modules · 1900 h

  1. 1.1Digital transformation of health systems
  2. 1.2Digital Health Strategy of the Spanish National Health System (SNS)
  3. 1.3Electronic health records and structured data
  4. 1.4Imaging and analytics workflows in the hospital
  5. 1.5Public and private actors in the sector
  6. 1.6Value in health and data-driven medicine
  7. 1.7Organisational barriers to digitalisation
  8. 1.8HIMSS digital maturity indicators
  9. 1.9Ethics of health data
  10. 1.10Case study: digital maturity diagnosis of a hospital
  1. 2.1Conventional and digital radiology
  2. 2.2Computed tomography and dose
  3. 2.3Magnetic resonance imaging and sequences
  4. 2.4Ultrasound and Doppler
  5. 2.5Nuclear medicine, PET and SPECT
  6. 2.6Mammography and tomosynthesis
  7. 2.7Optical and endoscopic imaging
  8. 2.8Image quality and artefacts
  9. 2.9Radiation protection and Royal Decree 1029/2022
  10. 2.10Practical: comparison of quality parameters across modalities
  1. 3.1DICOM format and metadata
  2. 3.2Preprocessing and intensity normalisation
  3. 3.3Filtering and enhancement
  4. 3.4Classical segmentation by thresholding and regions
  5. 3.5Multimodal image registration
  6. 3.63D reconstruction of anatomical structures
  7. 3.7Volume visualisation and surface rendering
  8. 3.8Radiomics and feature extraction
  9. 3.93D Slicer and ITK tools
  10. 3.10Laboratory: organ segmentation with 3D Slicer
  1. 4.1Hospital data architectures
  2. 4.2Data warehouses and data lakes
  3. 4.3OMOP and i2b2 models
  4. 4.4Source integration through FHIR
  5. 4.5PACS, VNA and image archiving
  6. 4.6Data quality and governance
  7. 4.7Integration of laboratory and pharmacy data
  8. 4.8Pseudonymisation for analytics
  9. 4.9Healthcare cloud platforms
  10. 4.10Practical: data model for a clinical dashboard
  1. 5.1Care and quality indicators
  2. 5.2Hospital dashboards
  3. 5.3Descriptive statistics of cohorts
  4. 5.4Kaplan-Meier curves in hospital cohorts
  5. 5.5Diagnosis-related groups
  6. 5.6Risk adjustment
  7. 5.7Effective visualisation of clinical data
  8. 5.8Benchmarking between centres
  9. 5.9Analysis of variation in practice
  10. 5.10Laboratory: emergency department dashboard in Power BI
  1. 6.1Clinical logistic regression models
  2. 6.2Trees, random forests and boosting
  3. 6.3Readmission prediction
  4. 6.4Early warning scores
  5. 6.5Internal and external validation
  6. 6.6Calibration and discrimination of models
  7. 6.7TRIPOD reporting guideline
  8. 6.8Bias and fairness of models
  9. 6.9Implementation in clinical practice
  10. 6.10Practical: readmission prediction model
  1. 7.1Convolutional networks for classification
  2. 7.2Segmentation with U-Net and nnU-Net
  3. 7.3Lesion detection
  4. 7.4Public imaging datasets
  5. 7.5Labelling and annotation by radiologists
  6. 7.6Data augmentation and transfer learning
  7. 7.7Explainability with activation maps
  8. 7.8Evaluation with Dice and AUC metrics
  9. 7.9Integration into the radiology workflow
  10. 7.10Laboratory: chest X-ray classification
  1. 8.1Unstructured clinical text
  2. 8.2Medical entity extraction
  3. 8.3Normalisation to SNOMED CT
  4. 8.4Language models for clinical Spanish
  5. 8.5Automatic ICD-10-ES coding
  6. 8.6Report summarisation
  7. 8.7Hallucination risks in generative models
  8. 8.8Evaluation of NLP systems
  9. 8.9Privacy in clinical texts
  10. 8.10Practical: extraction of diagnoses from reports
  1. 9.1Rule- and guideline-based systems
  2. 9.2Prescribing alerts
  3. 9.3Integration with the health record
  4. 9.4CDS Hooks and SMART on FHIR
  5. 9.5Alert fatigue
  6. 9.6Clinical impact assessment
  7. 9.7Clinician-centred design
  8. 9.8Liability in assisted decisions
  9. 9.9Knowledge base maintenance
  10. 9.10Workshop: decision support rule for sepsis
  1. 10.1Imaging software as a medical device
  2. 10.2High-risk AI systems in hospitals
  3. 10.3Implications of the EHDS for hospitals
  4. 10.4GDPR and secondary use of data
  5. 10.5Biomedical research with healthcare data
  6. 10.6Ethics and data committees
  7. 10.7Cybersecurity of hospital data systems
  8. 10.8Algorithmic transparency towards clinicians and patients
  9. 10.9Digital health technology assessment
  10. 10.10Case study: ethical assessment of a screening algorithm
  1. 11.1Identification of high-value use cases
  2. 11.2Project management in hospitals
  3. 11.3Multidisciplinary data and clinical teams
  4. 11.4MLOps model deployment
  5. 11.5Performance monitoring
  6. 11.6Change management with professionals
  7. 11.7Return on investment in clinical analytics
  8. 11.8Scaling to the care network
  9. 11.9Communicating results to medical management
  10. 11.10Case study: deployment plan for an imaging model
  1. 12.1Clinical problem and use case
  2. 12.2Data and imaging sources
  3. 12.3Data preparation and quality
  4. 12.4Development of the analytical model
  5. 12.5Retrospective validation with local data
  6. 12.6Dashboard or integration
  7. 12.7Ethical and regulatory analysis
  8. 12.8Hospital implementation plan
  9. 12.9Master's Thesis report
  10. 12.10Master's Thesis: defence before a panel
Methodology, software & labs

How you'll learn

100% online training on the SEIUM campus, with downloadable materials, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within each intake, so the programme is compatible with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is 3D Slicer, Python with PyTorch and MONAI, Power BI, OHDSI ATLAS and R, in free, academic or evaluation versions. Practical work is based on real procedures: segmentation and analysis of medical images, development of predictive models with clinical data and design of hospital dashboards. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: Regulation (EU) 2017/745 on medical devices, Regulation (EU) 2024/1689 on artificial intelligence, Regulation (EU) 2016/679 (GDPR), Regulation (EU) 2025/327 on the European Health Data Space, DICOM PS3, HL7 FHIR R4, Royal Decree 1029/2022, Law 14/2007 on Biomedical Research.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of the MDR, the EU AI Act and the GDPR applied to real cases, not as a theoretical annex.
Industry software:3D Slicer, Python with PyTorch and MONAI, Power BI, OHDSI ATLAS and R, in free, academic or evaluation versions.
Master's Thesis with defence:a clinical analytics or imaging project, from clinical problem and data sources to model development, retrospective validation, dashboard or integration, ethical and regulatory analysis and hospital implementation plan, defended before a panel.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Image segmentation
Segment an organ in 3D Slicer, from DICOM data to 3D reconstruction.
Clinical data model
Design the data model and an emergency department dashboard in Power BI.
Predictive model
Develop and validate a hospital readmission prediction model.
Imaging AI
Train and evaluate a chest X-ray classifier with Dice and AUC metrics.
Master's Thesis
A clinical analytics or imaging solution with retrospective validation, ethical and regulatory analysis and implementation plan, defended before a panel.
Career opportunities

Where this takes you

Medical imaging engineerClinical data scientistDigital health analystHealthcare AI engineerClinical information systems managerHealthcare digital transformation consultant
Admissions & scholarships

Requirements

  • University degree in engineering, science or architecture, or equivalent demonstrable professional experience.
  • Internet connection and a computer capable of running the programme software.
  • English sufficient to read technical standards.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

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FAQ

Frequently asked questions

The programme totals 1,900 hours over 12–14 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with 3D Slicer, Python with PyTorch and MONAI, Power BI, OHDSI ATLAS and R, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.

A SEIUM Institute diploma, awarded on passing the continuous assessment and the Master's Thesis. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.

Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.

A university degree in engineering, science or architecture (or equivalent professional experience), a computer able to run the software, and English sufficient to read technical standards.

Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.

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