Target group
- SMEs, research, data scientist, start-ups, health insurance companies, regulatory authorities, healthcare, education
Your requirements
- You need advice on implementing explainable machine learning models for clinical applications (XAI implementation and interpretation)
- You need advice on explainable and interpretable AI (XAI interpretation)
Our offer
Expertise in the innovative and conventional implementation of explainable and interpretable machine learning methods for training and testing data from electronic patient records (e.g. vital parameters, laboratory, imaging (MRI, CT), medication), biosignals (e.g. ECG, EEG), routine data or experimental data.
Explainability in AI encompasses methods that help users understand the decision-making processes of AI systems, enhancing their transparency and interpretability.
This is crucial for building trust, especially in clinical settings. Even for ML-models with high-performance metrics, users often have questions: “Why did you make that decision? When can I trust your predictions?”
Explainable AI (XAI) provides answers, allowing users to understand decisions and identify when to trust predictions. XAI highlights key variables influencing AI outputs and can validate causal relationships.
For instance, saliency maps (an XAI visualization method) can show whether COVID-19 predictions are based on relevant patterns in lung images rather than irrelevant details, reinforcing the model’s credibility. We can help you implement AI and XAI methods for your project data, ensuring trustworthy and transparent results that align with the newly approved AI ActRequirements
Activity in the health sector
