AI in medicine from scratch

The AI in Medicine – Fundamentals course is an intensive two-day training that combines theory with practice, focusing on real applications of artificial intelligence in healthcare.

Duration

16h · 2 days

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Who it's for

Ideal for teams that…

1 Physicians and healthcare professionals

2 Healthcare managers

3 IT specialists

4 R&D staff from medtech companies

Outcomes after the program

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

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AI fundamentals – Understand the differences between AI, ML, and DL, and how these technologies work in medicine

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AI applications in healthcare – Learn how AI supports diagnostics, medical image analysis, disease risk prediction, and clinical decision support

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Challenges with medical data – Learn how to prepare data for AI analysis, identify errors, and handle sensitive data properly

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Ethical and legal aspects – Understand the challenges AI faces in healthcare, including accountability, legal regulations, and privacy issues

Requirements

What you should know before we start

Basic knowledge of Python programming
Basic understanding of machine learning

Program

What we actually do

· 1. Key concepts: AI, ML, DL Definitions: artificial intelligence, machine learning, deep learning History of AI with a focus on medical applications
· 2. AI applications in healthcare Diagnostic imaging (radiology, ultrasound) Natural language processing (electronic health records) Clinical prediction (rehospitalization risk, identifying high-risk patients)
· 3. Medical data as the foundation of AI Types of data: imaging, text, numerical Data standards: HL7, FHIR, DICOM, ICD Challenges: data quality, missing data, sensitive data Anonymization, pseudonymization, and legal compliance
· 4. Overview of existing AI solutions in healthcare Commercial tools and platforms: Aidoc, PathAI, IBM Watson Health, BioMind Open-source and research projects: MONAI, Google Med-PaLM, BioGPT Performance and limitations of AI models on real data
· 5. Ethical aspects of AI in medicine Professional and legal responsibility Algorithmic transparency (explainability) Bias, fairness, and risk of discrimination
· 6. Regulations and legal standards AI Act (EU), MDR, FDA, HIPAA GDPR and patient data protection in AI models
· 7. No-code and low-code AI tools Platforms for building models without coding Creating predictive models with medical data Visualization and interpretation of results
· 8. Deployment and practical aspects of AI Architecture of clinical decision support systems Managing the AI model lifecycle Case studies of implementations in Poland and worldwide

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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What does the team need?

How we handle your data

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