--- title: "Healthcare AI: Clinical Data, Annotation, Systems" url: https://ypai.ai/solutions/healthcare/ description: "YPAI builds healthcare AI, from clinical documentation to assistants, and delivers clinical speech, imaging annotation and evaluation behind it. EEA by default." source: "src/copy/routes (route /solutions/healthcare/)" --- # AI can give care its time back. We build what makes it real. > YPAI builds healthcare AI, from clinical documentation to assistants, and delivers clinical speech, imaging annotation and evaluation behind it. EEA by default. Clinical speech and imaging data, expert annotation and evaluation, and AI systems built for defined healthcare workflows. From one Norwegian team, under GDPR, in the languages your patients speak. MODEL FLAG · FOR CLINICIAN REVIEW Data & Annotation · Evaluation · AI Implementation ## Care is losing hours it can get back. Three questions decide who gets them. Documentation that writes itself. Findings flagged while they still matter. Tools that speak the patient's language. Whether that arrives in your clinic comes down to what the model learned from, and how it was tested. ### Where do your clinical hours go? - Into documentation. Ambient clinical AI returns those hours to the encounter, when the model has heard real consultations: interruptions, dialect, drug names spoken aloud, the way clinicians actually talk. That is trained, not assumed. ### Has it seen findings like yours? - A diagnostic model is only as good as its labels. Radiology, cardiology and pathology annotation with credentialed reviewers, task protocols and QA records is what separates a model that assists from one that guesses. ### Does it speak your patients' language? - Danish, Norwegian, Swedish and Finnish clinical speech cannot be scraped at volume. Collection from native-speaker contributors, with documented consent, is the only source. If your tools should work here, the data has to be made here. ## Building clinical AI? Or buying it? Both doors are ours. ### You are building the model. - Clinical speech collection, medical imaging annotation, EHR and clinical text structuring, evaluation and red-teaming. Bought per engagement, delivered with provenance. ### You have the use case. - A defined clinical workflow turned into an operational AI system: assistants over your documentation, document flows, review queues. Built, integrated and tested before it touches care. ### The system and its data, one accountable team. When the model misses, the people who built it fix the data it learned from. No hand-off between a model vendor, an integrator and a collection company. 150+ languages, including all Nordic. Operated from Norway. ## The clinical data work, opened item by item. Run for healthcare AI teams, scoped per engagement, and delivered with the records that let you defend it. ### Clinical speech and ambient corpora - The consultations, dictations and commands an ambient documentation model must have heard. - Contributor-sourced clinical speech: consultation-style dialogue, structured note material and clinician-spoken commands, captured with consent and rights controls configured for the engagement. - Speaker-diarised, verbatim or clean-read transcription with clinical terminology, dialect coverage and QA on every batch. Speaker A - **Source**: Consented contributor · native speaker - **Audio**: Consultation dialogue · two speakers - **Transcript**: Diarised · clinical terminology - **Consent**: confirm. Documented · on record Delivered with provenance on every file ### Medical imaging annotation - Expert-reviewed labels for the findings your model must not miss. - Radiology, cardiology and pathology annotation with credentialed reviewers, a task protocol agreed up front, and QA records in the delivery package. - Reviewer qualifications, consensus stages and acceptance criteria set per engagement, for regulated model programmes. - **Study**: Routed to a credentialed reviewer - **Model flag**: flag. Finding proposed · for review - **Reviewer**: confirm. Credentialed · ruled Label delivered · QA record attached ### Clinical text and EHR structuring - Notes and records structured for training, with the de-identification method stated. - Clinical text extraction, classification and structuring against an agreed delivery schema, exceptions and hard cases included. - De-identification methods, limits and re-identification risk stated per engagement. - **Source**: Clinical note · unstructured - **Extracted**: Condition · medication · dosage - **De-identified**: flag. Direct identifiers removed · limits stated ### Clinical model evaluation and red-teaming - Proof that the model holds up on clinical language before a clinician relies on it. - Native-speaker evaluation of clinical speech and medical language models, with safety-critical test material and edge cases that matter in care. - Human review of model output by evaluators calibrated on the actual clinical domain, reported per batch. Reference - **Case**: Safety-critical · medication question - **Model answer**: Scored by a native-speaker evaluator - **Verdict**: flag. Failed the case · logged Reported per batch · regression on change ### Dataset licensing - Rights-cleared clinical corpora where collection is unnecessary. - Ownership, consent and commercial rights checked at scoping. Where the rights setup does not hold, we say so and collect instead. - Licensed delivery with the consent and rights documentation in the package. - **Corpus**: Clinical speech · existing - **Rights**: confirm. Ownership and consent checked Licensed · documentation in the package ## Nordic clinical language cannot be scraped. So we make it, word by consented word. Danish, Norwegian, Swedish and Finnish clinical encounters: consultation transcription, structured note material and clinician commands. A person signs off each stage in the chain. ### A native speaker, consented - Contributor-sourced clinical speech in Danish, Norwegian, Swedish and Finnish, with GDPR Article 7 consent and Article 9 biometric handling documented per contributor. ### A clinical ear on every batch - Diarisation and transcription with clinical terminology and dialect coverage, QA on every batch, de-identification with its limits stated. ### A native speaker rules on the model - Evaluation of clinical speech and language models against Nordic clinical language and the edge cases that matter in care, reported per batch. Scope, languages and QA stages are set per engagement. ## When the deliverable is the working system, not the dataset. A defined clinical use case, turned into a system that runs inside your workflow and stops where your clinicians rule. Built, integrated, evaluated and monitored by the team that stays accountable for it. ### Clinical documentation workflows - Ambient and dictation-fed documentation flows that draft the note and leave the clinician the verdict. ### Assistants over medical documentation - Retrieval-grounded assistants that answer from your approved documentation, with source traceability and human handoff. ### Document and records flows - Extraction, classification and routing for clinical documents, with review queues where judgement belongs. ### Human-in-the-loop review - Review stations for model output in care settings: queues, escalation and an audit trail on every ruling. Delivered with acceptance testing, monitoring and rollback, and with the technical documentation your GDPR and EU AI Act obligations ask about: what it learned from, how it was evaluated, where the human oversight sits. ## Words spoken in a clinic deserve the strictest room in the house. A Norwegian company under GDPR. Clinical project data is stored in Europe by default and processed in the EEA by default, with the paperwork to prove both. US healthcare work is handled HIPAA-compliant. - **Jurisdiction**: Norway · GDPR-native - **Storage**: European by default - **Processing**: EEA by default - **Consent**: Documented per contributor - **US engagements**: HIPAA-compliant handling - **Contracts**: Standard DPA terms · SCCs available - **Erasure**: 30-day end-of-contract SLA - **Withdrawal**: Workflow with audit trail - Safeguards for US healthcare speech work - [Data residency in the EEA](https://ypai.ai/data-residency-eea/) ## Four documents. Pick who is asking. Every healthcare engagement gets inspected: by privacy, by procurement, by the clinician who has to trust it, by the team that has to train on it. These pages are written for that inspection. ### [Healthcare data collection](https://ypai.ai/solutions/healthcare/custom-data-collection/) How PHI is de-identified and audited before it trains anything. What a strategic assessment scopes before the engagement starts. Which specialists label clinical data, and to what standard. DICOM, EHR and multimodal sourcing for a clinical model programme. ### [Medical imaging annotation](https://ypai.ai/solutions/healthcare/medical-imaging-annotation/) - Governance and decision evidence per label What governance evidence ships with every labelled study. The delivery gate and the quality metric you can hold us to. Who reviews the labels, under which review model. Quality metrics and decision evidence per batch, radiology to pathology. ### [De-identifying clinical audio](https://ypai.ai/blog/compliance/how-to-de-identify-audio-data-hipaa/) - The 18 HIPAA identifiers, in audio The 18 identifiers, the two lawful methods, and the residual risk. What documentation and audit trail the method leaves behind. What stays in the recording once identifiers are removed. Automated PHI detection and redaction, and where it needs a person. ### [Speech data for ambient clinical AI](https://ypai.ai/solutions/healthcare/hipaa-compliant-speech-data/) - Regulatory posture under GDPR and the EU AI Act Each requirement, its legal basis, and what we deliver. Where clinical AI procurement breaks, and the FAQ written for it. What high-fidelity ambient clinical speech has to capture. The methodology from consent-gated capture to delivery, step by step. ## Start with one workflow, one clinic, one language. We scope against your workflow, write the specification with you and test the work on a pilot batch before anything reaches care. Scope · Specification · Pilot batch · Acceptance · Production A bounded pilot carries its own acceptance criteria. You judge the output against your own clinic. Briefs are treated as confidential. We are used to data that cannot leave the EEA and projects that cannot be named. Tell us the workflow, the languages, and the data or system you need. We reply with a feasibility read. We come back with a feasibility read on your workflow and data. Enquiry details are treated as confidential.