Healthcare
In a hospital, AI can help assess images, pull the relevant information out of clinical records and notice early when a patient's condition starts to change. The clinician should see what each suggestion is based on, and patient data should not leave the hospital's own infrastructure.
Challenges
Patient data stays in the hospital
Patient records are among the most sensitive personal data and often have to stay on the hospital's own infrastructure. Models have to be trained and run there too.
Approval comes first
Work with patient data needs ethics committee approval, and systems used in diagnosis or treatment can fall under medical device rules. Both shape the project from the start.
Clinicians see the evidence
A clinician can weigh a suggestion when they can see the image region, record or values behind it. Results can differ between hospitals, so accuracy is measured separately at each site.
Typical problems
Medical imaging
Detecting and measuring findings in X-ray, CT, MRI and pathology images, and flagging studies that may need an earlier read. The radiologist or pathologist still assesses every study.
Clinical documents and records
Turning discharge summaries, reports and notes into structured data, and assistants that search a patient's records and give each answer with its source.
Patient monitoring and early warning
Tracking vital signs and lab results over time and alerting the care team early to patients whose condition may be getting worse.
Hospital operations and capacity planning
Forecasting patient numbers, bed occupancy and operating room demand, and supporting staff and resource planning.
Research and multi-center studies
Preparing data for clinical research and training models on data from several hospitals without patient records leaving any of them.
Our research areas
See all research areasWorking with sensitive data
Training and running models on patient records without the data leaving the hospital, including studies across several centers.
Multimodal understanding
Reading images, clinical text and monitoring data about the same patient together.
Interpretability & evaluation
Showing what each suggestion is based on, and measuring accuracy separately at each hospital.
Learning from limited data
Learning from a small number of clinician-labeled cases together with the many records that have no labels.
Related services
All services- Computer visionSystems that find, count, measure and inspect objects and events in images and video.
- Multimodal AIApplications that combine text, images, audio and documents to complete one defined task.
- AI assistantsAssistants that answer from your own documents and systems, with sources and access control.
- Time series forecastingForecasts of demand, load, capacity and sensor values that show how certain they are.
- Synthetic dataGenerated training and test data for cases that are rare, sensitive or missing in your real data.
- LLM deploymentLanguage models running on your own servers or private cloud, sized and secured for your workload.
Work with us
Work usually starts with one department or one clinical question, with a clinician who judges the results, and the system is installed on the hospital's own infrastructure. Once the results are measured, the scope can extend to other departments or hospitals.
R&D projects
For problems with no proven solution yet. We test approaches on your data and work toward a prototype in agreed milestones. At each milestone we tell you plainly what looks feasible and what doesn't.
Software development & consulting
For problems with known solutions. We build production software on proven methods, or advise your team on architecture, tooling and model choice.
Funded research
Joint R&D projects backed by national and international research funding, with industry and academic partners.
Corporate training
Training programs tailored to your team. Topics include everyday work with AI assistants, product development, integration and strategy.
Frequently asked questions
Does patient data leave the hospital?
No. Models run on the hospital's own servers, and patient records stay where they are. Personal data is processed in line with KVKK and, where relevant, GDPR.
Does AI make the diagnosis?
No. The system highlights findings, prepares information and gives early warnings. Diagnosis and treatment decisions always stay with the clinician.
Does an AI project in a hospital need ethics committee approval?
Usually yes, when patient data is used for research. Systems used directly in diagnosis or treatment may also fall under medical device rules, and this is clarified at the start.
Where should a hospital start with AI?
Usually with one department and a clear question whose results are easy to measure, such as a common type of imaging study or a frequently used document.
Who runs the system after handover?
The hospital's own IT and clinical teams. The system is handed over with its documentation, and we can also provide support after that if needed.
From our blog
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Synthetic data can help when real data is scarce, sensitive, or unavailable. When is it worth using, and how can you tell whether it improves results?
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Will a proven AI method work on your data?
A proven AI method can still fail on your data and workflow. Testing shows whether published evidence transfers to your specific constraints.
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How do you prepare data for LLM fine-tuning?
Turn existing data into useful fine-tuning examples: format, chat templates, cleaning, deduplication, redaction, synthetic data, and pre-training checks.
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