The Complete Guide to Using AI in the Healthcare Industry in Norway in 2025

In 2025 Norway stands at a practical inflection point: a strong legal framework anchored in the Personal Data Act/GDPR, a National Digitalisation Strategy aiming to build a national AI infrastructure by 2030, and targeted public investment (including a NOK1 billion research fund) have moved AI in health from lab experiments to clinical workflows and telehealth at scale.

Hospitals are already deploying proven tools - Vestre Viken Health Trust's rollout of Philips' cloud-based AI Manager (an AI bone-fracture app serving roughly half a million people across 22 municipalities) shows how imaging AI can cut wait times and free radiologists for complex cases - while regulators run sandboxes and issue sector guidance to tame risks.

That mix of clinical value, data‑protection duties and upcoming EU rules means Norwegian health teams must pair careful governance with practical skills; for clinicians and managers learning usable AI tools, the AI Essentials for Work bootcamp offers a 15‑week, workplace-focused syllabus to get started (see course details and registration).

For a concise legal and market snapshot, explore Norway's AI legal landscape and the Vestre Viken deployment.

“Applying Artificial Intelligence in our Radiology Department has surpassed our expectations. Besides improving patient flows, and quality of care to our patients, we have found that AI even finds fractures that doctors overlooked.” - Cecilie B. Løken, Vestre Viken Health Trust

Norway's regulatory picture in 2025 is pragmatic and fast-moving: as an EEA state the EU's landmark AI Act will be brought into Norwegian law, and Norway has already picked the practical building blocks for enforcement and guidance - the Norwegian Communications Authority (Nkom) is named the national coordinating supervisory authority and Norsk akkreditering will handle technical accreditation, while a new “AI Norway” expert environment inside the Norwegian Digitalisation Agency (Digdir) will stitch together sandboxes, advice and public‑sector pilots to help hospitals and startups navigate the rules; see the government announcement on establishing AI Norway and Nkom's supervisory role.

That architecture reflects Norway's risk‑based, technology‑neutral approach (underpinned by the Personal Data Act/GDPR) and the timelines tracked in the EU AI Act implementation plans - national authorities must be designated as part of wider EEA preparations and Norway expects a law implementing the AI Act in the summer of 2026; follow the AI Act national implementation plans for the evolving timeline.

For clinicians and procurement teams the takeaway is clear: expect coordinated sector guidance, an operational sandbox culture for testing, and a central regulator (Nkom) acting as Norway's “single point of contact” with EU bodies - a tidy national governance backbone that still leaves real work to hospitals and vendors to document data flows, risk assessments and human oversight before deployment.

“The Government is now making sure that Norway can exploit the opportunities afforded by the development and use of artificial intelligence, and we are on the same starting line as the rest of the EU.” - Karianne Tung, Minister of Digitalisation and Public Governance

Norway has moved from isolated pilots to a coordinated national push: the Norwegian Directorate of Health now leads a joint AI plan (2024–2025) that explicitly aims to scale safe, clinically‑useful AI across both municipal and specialist care while protecting patients and staff, from validation guidance to risk assessments for large language models and a programme of thematic seminars to keep the work practical and current - read the plan's summary for the action areas and timelines.

This sector strategy is tightly connected to broader government goals in The Digital Norway of the Future, which stresses cross‑sector governance, shared digital infrastructure and public‑private collaboration as prerequisites for scaling AI safely; the strategy even earmarks measures to adapt language models to Norwegian conditions and to strengthen competence across managers and clinicians.

The approach is pragmatic: accelerate adoption where benefits are clear (including administrative automation and clinical decision support), run cross‑agency guidance and sandboxes, and coordinate procurement and standards so hospitals and municipalities can adopt proven tools without reinventing the governance wheel - a sensible roadmap that pairs concrete implementation tasks with national coordination and European alignment.

Practical implementation in Norwegian hospitals starts with smarter tenders and ends with disciplined post‑market vigilance: Sykehusinnkjøp HF's category strategy shows how procurement teams are now matching contract models to a product's life‑cycle, balancing clinical input, sustainability and implementation support to make adoption faster and more reliable (Sykehusinnkjøp HF pharmaceutical strategy).

For medical devices, the proposed New Methods updates (Apr 2025) push earlier assessment, clearer entry routes and closer coordination between suppliers, regional health authorities and regulators so procurements and HTA work run in step (proposed New Methods changes).

Compliance and market access remain practical gatekeepers: devices must follow CE conformity procedures, have a named compliance contact and Norwegian labelling, and hospitals must plan for mandatory reporting and field‑safety actions under national device rules - see the Norway device registration guidance for timelines and responsibilities (Norway medical device registration & post‑market rules).

On the ground, that translates into tender scoring that rewards total cost of care and outcomes (not just price), embedding clinical feedback loops and prescription‑support systems, and using contract levers such as risk‑sharing (for example, suppliers obliged to cover early replacement surgery) so the

so what?

is clear: a more expensive implant can be the cheaper, safer choice if it reliably prevents reoperations.

Deploying LLMs and other AI in Norwegian healthcare sits squarely inside the Personal Data Act (PDA) - Norway's implementation of the GDPR - so clinicians and procurement teams must combine clinical risk assessment with solid data‑protection practice: use a lawful basis (consent, public‑interest/healthcare tasks or, more rarely, legitimate interests), treat health data as a special category under Article 9, and appoint or consult a DPO where processing is large‑scale or core to the organisation's activities; see the concise PDA overview for duties and DPIA triggers.

Large language models raise three practical red flags under recent guidance: the bar for claiming that a model is “anonymous” is high (supervisors will expect evidence you cannot extract training subjects' data), relying on legitimate interest requires a rigorous necessity and balancing test, and unlawful processing during model development can taint downstream deployment - the EDPB opinion spells out these points and practical mitigations.

Operational steps that map to legal requirements: run a DPIA early (LLMs and large‑scale training often land on Datatilsynet's DPIA “blacklist”), embed data‑protection‑by‑design and strong Article 32 security measures (pseudonymisation/encryption, access controls), document transfer safeguards (SCCs or adequacy routes post‑Schrems II), and prepare breach playbooks - the 72‑hour notification clock to Datatilsynet can feel as urgent as an ECG alarm in a ward.

Taken together, the legal and technical guidance means hospitals should treat LLM projects as clinical programmes with governance, not just IT experiments; for the legal foundations and the EDPB's AI‑model guidance, consult the PDA overview and the EDPB opinion on AI models.

Risk, bias and oversight are now front‑and‑centre for Norwegian healthcare AI: the Directorate of Health's joint AI plan stresses preventing unfair outcomes and embedding patient and clinician involvement from procurement to deployment, while national guidance urges practical safeguards for large language models and other systems (Norwegian Directorate of Health Joint AI Plan 2024–2025 summary).

Real-world experience from the Datatilsynet sandbox shows how this plays out: Akershus University Hospital's EKG AI - trained on roughly 100,000 EKGs - became a test case for fairness, exposing familiar causes of algorithmic bias (unrepresentative data, design choices and misuse in practice) and highlighting three pragmatic remedies - check the data, train staff and monitor post‑training performance - that Norwegian projects must build into clinical governance (Akershus University Hospital EKG AI Datatilsynet sandbox exit report).

Industry thinking echoes this: fairness requires diversity in people, data and validation, plus continuous monitoring and robust quality systems to prevent models from encoding past inequities (Philips blog on fair and bias‑free AI in healthcare).

Practically, that means legal checks (GDPR/Equality rules and Article 22 safeguards), clinical trials or consented data collection where needed, CE/medical‑device pathways for decision‑support tools, and clear post‑market oversight so a model that looks good in a lab doesn't quietly under‑serve real patients - especially minority groups whose ethnicity may not even appear in the training set.

Contracts and standards are where legal risk meets procurement reality in Norway: the long‑standing Product Control Act already imposes an expansive duty of care on producers, importers and service providers to evaluate safety, share traceability information, notify authorities and support recalls or withdrawals when a product risks health, and it carries criminal penalties and coercive fines for non‑compliance - see the government's Norway Product Control Act (official government text) for the detail.

On the horizon, the EU's revised Product Liability Directive brings a sea‑change for digital health: software and certain digital services are explicitly treated as “products”, exposing suppliers to potential strict liability (no fault required) for defects, including failures to deliver security updates or to patch vulnerabilities - rules Norway is expected to transpose, so procurement teams should plan for tougher vendor obligations and new evidentiary rules that make it easier for claimants to prove defectiveness.

Practically this means tighter contract terms (mandatory update schedules, clear liabilities for cybersecurity lapses, explicit post‑market surveillance and warranty periods), stronger supply‑chain documentation and insurance checks, and procurement scoring that rewards demonstrable lifecycle governance; in short, buying AI in 2025 Norway looks less like a one‑time purchase and more like signing up for an ongoing clinical safety partnership - because a missed update on an AI triage system can cost far more than license fees.

For a clear legal primer, consult the EU Revised Product Liability Directive briefing (legal analysis) and broader product‑liability summaries when drafting tenders and SLAs.

Standards and certification are no longer optional checkboxes for Norwegian health providers - they are the backbone of safe AI deployment, and regulators expect hospitals to plug into national systems and proven international profiles.

Norway's National Cyber Security Centre (NCSC) and its NorCERT unit are the national point of contact for severe ICT incidents and run 24/7 technical threat operations and forensics, so hospitals should assume coordinated incident response and reporting will be enforced (Norwegian National Cyber Security Centre (NCSC) - NCSC Norway official site).

At the infrastructure level, the Norwegian Health Network (NHN) has mandated the FAPI 2.0 security profile across the sector, bringing banking‑grade API protections - automated conformance testing, phased migration and measures like DPoP that can render stolen authentication tokens cryptographically useless - into everyday health IT (NHN adopts FAPI 2.0 for Norwegian healthcare data security).

Organisations should also map ISO 27001 or equivalent frameworks into clinical IT procurement and evidence continuous compliance, because certification plus live security operations (CERT collaboration, incident drills and supply‑chain controls) is what keeps AI systems both available and trustworthy.

The practical result: expect mandatory standards in tenders, automated conformance checks for APIs, and a duty to integrate with national incident channels rather than rely on ad‑hoc vendor fixes - treating cybersecurity as an ongoing clinical-safety task, not a one‑off IT project.

“FAPI 2 has already delivered tangible security gains,” noted Ragnhild Varmedal, CTO for HelseID.

Norway's AI ecosystem for health is maturing fast because the money is finally matching the policy: the Research Council of Norway alone has backed some 470 AI projects (about NOK 2.2 billion) and runs rolling schemes - from FRIPRO to innovation and commercialisation calls - designed to take ideas from pilot to market (Research Council of Norway calls overview).

Strategic, targeted bets are visible too: Sigma2 won NOK 200 million to co‑finance a new national supercomputer (a Betzy successor) and the government set aside NOK 40 million specifically for Norwegian language models to support projects like Mimir, which together boost the compute and data resources researchers need (Sigma2 funding details).

Norway is also funding global stewardship and capacity building in AI for health through a NOK 45 million grant to HealthAI, while thematic centre grants - such as a NOK 173 million award for the MishMash Centre for AI and Creativity - show the breadth from clinical tools to creative AI research.

These stacked instruments - infrastructure, centre funding, rolling researcher grants and international calls - mean hospitals and startups can tap diverse streams for pilots, validation studies and scale‑up, and that a Norwegian team with the right proposal can pursue national, Nordic and EU-genAI calls in parallel.

“AI should be a public digital common good. Better regulation is essential to promote secure and ethical AI solutions.” - Anne Beathe Kristiansen Tvinnereim, Minister of International Development

Conclusion & beginner's checklist: Norway in 2025 offers a clear playbook - start with governance, then prove value: 1) ground the project in the Norwegian Directorate of Health Joint AI Plan 2024–2025 (summary) to define the clinical problem and end‑user needs (Norwegian Directorate of Health Joint AI Plan 2024–2025 (summary)); 2) run a DPIA and document your lawful basis under the Personal Data Act/GDPR early (treat health data as special category data); 3) confirm whether the tool is a medical device (CE/MDReg pathway) and map compliance to upcoming AI Act obligations - consult the Chambers practice guide legal primer on AI in Norway for liability, product‑safety and automated‑decision requirements (Chambers Artificial Intelligence 2025 - Norway legal primer); 4) design procurement and contracts for lifecycle governance (mandatory updates, post‑market surveillance, clear performance metrics) and plug into national procurement platforms and standards; 5) embed cybersecurity, interoperability and conformance testing from day one; 6) pilot with representative local data, check for bias, and set continuous monitoring to catch model drift; and 7) build competence across clinicians, managers and vendors - practical workplace training (for example, the 15‑week AI Essentials for Work bootcamp) helps translate policy into safe daily practice (AI Essentials for Work bootcamp (15-week) - syllabus & registration).

Treat AI projects as clinical programmes - not IT side‑projects - and remember the 72‑hour breach clock and post‑market duties can feel as urgent as an ECG alarm in a ward.

“Ivalua's solution will allow us to manage the entire procurement process from one joint platform. The solution will allow further digitization of our processes and simplify the working dialogue with both the health trusts and suppliers.” - Bente Hayes, CEO of Sykehusinnkjøp HF