Postgraduate Diploma in Ethics and Governance of Algorithms in Devices
Online 800-hour Postgraduate Diploma in Ethics and Governance of Algorithms in Devices. 8 modules covering Regulation (EU) 2024/1689 (Artificial Intelligence Act), Regulation (EU) 2017/745 (MDR) and Python (scikit-learn). Assigned tutor and open enrolment.
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Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Identify the uses of algorithms in medical devices and their risks.
Analyse the ethical dilemmas of algorithmic decision-making.
Interpret the obligations of the AI Act and the MDR.
Establish data governance and model lifecycle governance.
Evaluate bias and performance across patient subgroups.
Design human oversight and transparency mechanisms.
Plan the monitoring of algorithms after deployment.
Who it's for
- AI governance lead looking to specialise in Ethics and Governance of Algorithms in Devices.
- Algorithm ethics specialist looking to specialise in Ethics and Governance of Algorithms in Devices.
- Clinical data scientist looking to specialise in Ethics and Governance of Algorithms in Devices.
- AI systems auditor looking to specialise in Ethics and Governance of Algorithms in Devices.
- Regulatory specialist in healthcare software looking to specialise in Ethics and Governance of Algorithms in Devices.
- Data protection officer looking to specialise in Ethics and Governance of Algorithms in Devices.
- Engineering or science graduates seeking to enter health technology.
What you'll work with
- Regulation (EU) 2024/1689 (Artificial Intelligence Act)
- Regulation (EU) 2017/745 (MDR)
- ISO/IEC 42001 (AI management systems)
- ISO/IEC 23894 (AI risk management)
- ISO/IEC TR 24027 (bias in AI systems)
- GMLP principles from FDA, Health Canada and MHRA
- Regulation (EU) 2016/679 (GDPR)
8 modules · 800 h
1.1Types of algorithms: rule-based, statistical and machine learning1.2Locked versus adaptive algorithms1.3Uses: diagnosis, triage, monitoring and dosing1.4AI-enabled devices authorised by FDA and CE1.5Lifecycle of a clinical algorithm1.6Specific risks of algorithms1.7Automation and clinical responsibility1.8Expectations of patients and professionals1.9Lessons from withdrawn algorithms1.10Case study: analysis of a sepsis detection algorithm
2.1Bias in training data2.2Fairness across demographic groups2.3Opacity and explainability2.4Patient autonomy and shared decision-making2.5Accountability for algorithmic errors2.6Automation bias among clinicians2.7Privacy and re-identification2.8Consent for data use2.9Equitable access to technology2.10Workshop: ethical analysis of a dermatology algorithm
3.1European AI Act and medical devices3.2High-risk classification under the MDR3.3MDCG guidance on AI in medical devices3.4GMLP principles from FDA, MHRA and Health Canada3.5Predetermined change control plan (PCCP)3.6ISO/IEC 42001 standard3.7BS/AAMI 34971 on AI risk3.8IEC 62304 applied to models3.9GDPR and automated decisions3.10Case study: regulatory map of radiology software
4.1Governance of datasets4.2Quality, representativeness and labelling4.3Documentation: datasheets and model cards4.4Separation of training and test data4.5Model version control4.6Management of data suppliers4.7Recording design decisions4.8Roles: model owner and committee4.9Lifecycle traceability4.10Practice: drafting a clinical model card
5.1Clinical performance metrics5.2Fairness metrics: parity and equality of opportunity5.3Subgroup analysis5.4External and multicentre validation5.5Model calibration5.6Robustness and distribution shift5.7Explainability: SHAP and saliency maps5.8TRIPOD-AI and CONSORT-AI guidelines5.9Prospective studies and clinical impact5.10Laboratory: fairness audit with Fairlearn
6.1Designing human oversight6.2Informing users about limitations6.3Explanation interface for clinicians6.4Communication with patients6.5Training for clinical users6.6Managing human–algorithm disagreement6.7Usage logs and decision traceability6.8Public transparency and algorithm registers6.9Patient involvement in governance6.10Workshop: designing transparency information
7.1Performance monitoring in real-world use7.2Detection of data and concept drift7.3Controlled model updates7.4Reporting AI-related incidents7.5Periodic algorithm audits7.6Complaint management and feedback7.7Fairness indicators in production7.8Withdrawal or deactivation of the algorithm7.9Follow-up reports7.10Case study: model drift after a scanner change
8.1Selection of the device and algorithm8.2Ethical and impact assessment8.3Regulatory analysis8.4Data governance plan8.5Bias evaluation plan8.6Design of human oversight8.7Post-market monitoring plan8.8Governance structure8.9Report and presentation8.10Final review with the tutor
How you'll learn
Fully 100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: students progress at their own pace within the intake, which makes it possible to combine study with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python (scikit-learn), Fairlearn, AI Fairness 360, SHAP, MLflow, in free, academic or evaluation versions. Case studies are based on real procedures for algorithmic bias audits, performance evaluation across population subgroups, and review of AI technical documentation. Assessment is continuous across each module, plus a final integrative project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Identify the uses of algorithms in medical devices and their risks.
Analyse the ethical dilemmas of algorithmic decision-making.
Interpret the obligations of the AI Act and the MDR.
Establish data governance and model lifecycle governance.
Evaluate bias and performance across patient subgroups.
Where this takes you
Requirements
- Prior technical training or professional experience in the sector. A university degree is not required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient Spanish to follow technical regulatory texts.
Ready to start?
Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.
Frequently asked questions
The programme comprises 800 hours in total, spread over 6 to 8 months. That is an average of 10 to 15 hours per week, compatible with full-time employment.
You work with Python (scikit-learn), Fairlearn, AI Fairness 360, SHAP and MLflow. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.
An SEIUM own diploma upon passing the continuous assessment and the integrative project. This is in-house training, not an official qualification from the Ministry.
Yes. Modules passed in a Course or Postgraduate Diploma in the same area are recognised towards the higher-level programme. Check the recognition policy when you enrol.
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