AI in Medicine – from predictive model to clinical implementation

This intermediate-level training is designed for participants who already understand the basics of artificial intelligence and want to develop their skills in the context of healthcare applications.

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Who it's for

Ideal for teams that…

1 Developers and data analysts in the medical sector

2 Data engineers and ML Ops professionals in healthcare

3 Physicians and researchers running AI projects

4 R&D employees in medtech and biotech companies

5 Participants who have completed an introductory AI training or have basic AI knowledge

Outcomes after the program

Hands-on AI and data analytics workshops — built around your team's real cases.

✓

Medical data processing – Load, transform, and prepare data to meet AI model requirements

✓

Building and training AI models – Create your own predictive models in Python and evaluate their performance

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Model validation and interpretation – Learn validation methods and techniques for interpreting models in a clinical context

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AI deployment in practice – Understand how to implement AI models in healthcare facilities while ensuring compliance with regulations and ethics

Requirements

What you should know before we start

Knowledge of Python (NumPy, Pandas, scikit-learn)
Basic understanding of ML and neural networks
Basic familiarity with medical data analysis (preferred: EHR, DICOM)

Program

What we actually do

· 1. Introduction and overview of the AI pipeline in medicine End-to-end AI project: from data to deployment The role of data, model, interpretation, and integration Advanced work with medical data Data preprocessing: EHR, CSV, DICOM Detecting and handling errors, gaps, and anomalies Data standardization according to HL7/FHIR
· 2. Building predictive models in Python (hands-on) Logistic regression, decision trees, random forest Deep learning in medical imaging Working with DICOM images – segmentation, classification CNN basics and applications in diagnostics
· 3. Explainable AI (XAI) SHAP, LIME, Grad-CAM – understanding model decisions Examples of model interpretation on clinical datasets
· 4. Ethics and responsibility in AI Explainability, bias, fairness Case studies of model errors and their clinical consequences
· 5. Law and regulations for AI in medicine AI Act (EU), MDR, FDA Technical documentation, validation, and system registration
· 6. MLOps and model lifecycle Validation, retraining, model monitoring
· 7. Case studies and deployment workshop Case study: deploying an AI model for patient triage Mini-project: implementing AI in a hospital setting

Every module is adapted to your stack and context. The above is a starting point — not a fixed agenda.

How we work

From brief to retro in 30 days.

Brief & diagnosis

A call with the team lead + a short survey for participants. We define goals, gap and context.

Program customization

We adapt modules, case studies and code examples to your stack. Approval in 5 days.

Workshop

Trainer-led sessions, hands-on, code review. Mentor available between sessions too.

Retro + report

Outcome report for the team and lead. 30 days of consulting included.

Inquiry

Send a brief. We'll reply within 1 day.

After a short brief we'll prepare a program and a quote. No obligations — it's just a starting point.

✓Quote within 48h of the brief

✓First session within 30 days

✓Pilot before the full decision

✓VAT invoice, payment in instalments possible

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