AI+ Doctor™

Price
Net: 397,00 €
VAT.: 75,43 €

Duration
1 day

For companies and job seekers:
this course is 100% fundable!

Location

Course Language
English

Training Solutions
WalkIn^®

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Medicine and artificial intelligence are rapidly converging—and this convergence is opening up a whole new field of diagnostic methods, smart decision support, and data-driven precision. Modern tools are paving the way for faster insights and well-founded medical procedures.

Key topics

AI-supported diagnostics and clinical decision-making models.
Automated evaluation of medical data.
Predictive analytics for treatment pathways.
Medical documentation with AI tools.
Security and compliance aspects in the healthcare environment.

Prerequisite
Basic understanding of medical processes and interest in digital technologies.

Target group
Professionals from the fields of medicine, research, or healthcare management who want to use AI structures safely and effectively.

AI is fundamentally changing medical practice—and creating new opportunities for more precise care, robust analyses, and innovative applications that sustainably strengthen everyday clinical
practice.

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course content

What is AI for doctors?

From decision support to diagnostic intelligence
What makes AI unique in medicine?
Types of machine learning in medicine
Common algorithms and their function in healthcare
Practical use cases in various medical fields
Debunking myths about AI in healthcare
Real tools used by clinicians today
Practical example: Medical image analysis with MediScan AI

AI in diagnostics and imaging

Introduction to neural networks: Unlocking the power of AI.
Convolutional neural networks (CNNs) for visual data: Seeing through the eyes of AI.
Image modalities in medical AI: The multimodal view of AI.
Model training workflow: From data labeling to deployment—The AI lifecycle in medicine.
Human-AI collaboration in diagnosis: The power of augmented intelligence.
FDA-approved AI tools in diagnostic imaging: Trust and validation.
Hands-on exercise: Exploring AI-assisted differential diagnosis with Symptoma.

Introduction to basic data analysis

Understanding clinical data types – EHRs, vital signs, laboratory results
Structured vs. unstructured data in medicine.
The role of dashboards and visualization in clinical decisions.
Pattern recognition and signal detection in patient data.
Identifying high-risk patients based on trends and AI scores.
Interactive activity: AI assistant for insights into clinical notes.

Predictive analytics and clinical decision support – For proactive patient care

Predictive models for risk stratification – sepsis and hospital readmissions
Logistic regression, decision trees, ensemble models
Real-time alerts – Early warning systems (MEWS, NEWS)
Sensitivity vs. specificity – selection of metrics according to clinical needs
Use cases for AI-driven interventions in the intensive care unit and emergency room.

NLP and generative AI in clinical use

Fundamentals of NLP in healthcare
Large language models (LLMs) in medicine
Prompt engineering in a clinical context
Use cases for generative AI – summaries, consultation scripts, translations
Ambient intelligence: Next-generation clinical documentation.
Limitations and risks of NLP and generative AI in medicine.
Case study: Transforming clinical documentation and improving patient care with Nabla Copilot.

Ethical and appropriate use of AI

Algorithmic bias – effects on race, gender, and socioeconomic status
Explainability and transparency (SHAP and LIME)
Validation of AI across population groups
Regulatory standards – HIPAA, GDPR, FDA/EMA compliance
Drafting ethical guidelines for the use of AI
Case study – Biased pulse oximetry detection

Evaluation of AI tools in practice

Key figures: Understanding the basics
Confusion matrix and interpretation of the ROC curve
Adapting key figures to the clinical context
Interpretation of AI results: Improving clinical decision-making
Critical evaluation of manufacturer specifications: Ensuring reliability and effectiveness
Warning signs with commercial AI tools: Recognizing and mitigating risks
Checklist: "10 questions you should ask before purchasing AI tools"
Practical exercises

Implementation of AI in clinical settings

Identification of department-specific AI use cases.
Assigning AI to workflows (preliminary diagnosis, treatment, aftercare)
Pilot planning: schedule, data, feedback cycles
Team roles – clinical champion, AI specialist, IT administrator
Monitoring AI errors – root cause analysis
Change management in clinical teams.
Example: Workflow in the emergency room with integration of triage AI
Scaling AI solutions across the healthcare system.
Evaluating the impact and performance of AI after implementation.

Your training will be 100% funded

Our funding schemes are available exclusively to participants who live or work in Germany. Funding is not available outside Germany.

For career starters, job seekers, career changers, startups, experienced professionals, those looking to advance their careers, specialists, etc.

The QCG program for everyone in the company

Further information

The education voucher for all job seekers

Further information

Frequently asked questions

What makes the AI+ Doctor™ course so relevant to medicine?

AI+ Doctor™ explains how AI supports medical practice—from diagnostics to patient communication. Practical, up-to-date, and clinically oriented.

Who benefits from the AI+ Doctor™ course?

Ideal for medical professionals, doctors, hospital staff, healthcare students, and anyone who wants to improve medical processes with AI.

How can AI+ Doctor™ be used in everyday work?

What you learn can be applied directly—in clinics, practices, or research. The course shows how AI systems can be used safely and effectively.

Do you need to know how to program?

No. You can get started even without prior IT knowledge. Medical understanding and an interest in digital solutions are more important.

Which AI tools and technologies will you learn about?

The course uses practical technologies such as Python, TensorFlow, and Scikit-learn to understand and apply AI-supported diagnostics, medical image analysis, and data-based decisions in clinical practice.

How does the course help when working with patient data?

He demonstrates how data can be processed in compliance with legal requirements, analyzed anonymously, and used effectively with AI—for better decisions in everyday life.

What distinguishes AI+ Doctor™ from general AI courses?

The focus here is on medical applications—with real-life case studies, industry-specific knowledge, and clinical relevance.

What are the advantages of certification?

The official AI+ Doctor™ certificate strengthens digital competence in the healthcare sector—visible to employers, patients, and research institutions.

Do you have any further questions? Please contact us.

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