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

Iceland's healthcare scene in 2025 is shifting from careful curiosity to practical experimentation: Reykjavik University and the University of Iceland are staging accessible briefings on AI and high‑performance computing in medicine (see the University of Iceland Introduction on AI), while leading Nordic scientists meet at the NSHG‑PM 2025 symposium in the magnificent Mærsk Tower to chart AI opportunities in biomedical research (NSHG‑PM 2025 Symposium on AI Opportunities in Nordic Healthcare).

Concrete wins already evidenced in reporting include faster genomic sequencing at DeCode Genetics that enabled targeted interventions and reduced costly blanket measures in Iceland (DeCode Genetics genomic sequencing turnaround case study), a memorable example of how research, policy dialogue and local expertise are converging to make AI a careful, practical tool for diagnostics and system efficiency.

Iceland isn't just talking about AI - practical uses are already woven into pandemic response and everyday care: contact tracing and exposure notification through the Rakning C‑19 apps, population‑scale genetic surveillance (DeCode Genetics sequenced all new SARS‑CoV‑2 cases, often within 2–3 days), and telemedicine workflows tied to electronic medical records that let clinicians monitor diagnosed patients remotely and classify status into green/yellow/red for targeted follow‑up (see the detailed account in the Directorate of Health review on COVID‑19 and AI Directorate of Health review on COVID‑19 and AI and Iceland).

Icelandic reporting also highlights how AI and models from abroad were used to project public‑health scenarios, while local innovation points toward patient‑facing tools - for example, scripts for multilingual chatbots handling appointments, lab results and Rakning C‑19 guidance that can reduce call‑center load and speed patient access to advice (patient‑facing multilingual healthcare chatbot example in Iceland).

For a vivid measure of scale: Iceland ran almost a quarter‑million domestic tests in 2020, pairing high testing intensity with rapid sequencing to turn data into targeted public‑health action rather than blunt, costly measures - a practical blueprint for how AI, apps and genomics can be combined in a small, well‑connected health system (DeCode Genetics rapid genomic sequencing case study and public‑health impact).

Iceland's health system is compact and highly structured: the Health Services Act (No. 40/2007) sets the blueprint for universal access and divides the country into seven healthcare districts so that primary healthcare -

the first stop

- for most patients - can be delivered close to home, with Landspítali University Hospital serving as the national hub for specialist care (see the Ministry's Life and Health overview Iceland Ministry of Health Life and Health overview on government.is).

Oversight and public‑health duties rest with the Directorate of Health, which handles everything from epidemic prevention to professional licensing, while Icelandic Health Insurance manages payments and reimbursements; medicines, medical devices and radiation safety are supervised by the Icelandic Medicines Agency and the Radiation Safety Authority, and the National Bioethics Committee vets health research to keep projects on sound ethical footing.

For the legal framework in English, the Health Service Act is available through the ILO/NATLEX entry, which is a useful reference when planning AI deployments that must align with existing laws (ILO NATLEX entry for the Health Services Act (No. 40/2007)).

This clear division of roles - local primary care, regional hospitals, a national university hospital and centralized regulators - creates both a practical runway for targeted AI pilots and defined touchpoints for governance and compliance.

Iceland's path to binding AI rules runs through the EEA: there's no separate “AI Act for Iceland” yet, because as an EEA state Iceland will implement the EU AI Act only once it is incorporated into the EEA Agreement - meanwhile Reykjavik has been active as an observer in EU governance forums while national plans are prepared (EU AI Act national implementation plans and overview, Iceland AI legal framework analysis - Global Legal Post).

Practically, that means Icelandic healthcare providers should watch three timing milestones already in force at EU level: the Regulation entered into force on 1 August 2024, a set of prohibitions and “AI literacy” duties began on 2 February 2025, and most substantive obligations (including obligations for high‑risk systems) become effective on 2 August 2026 (Alston Privacy: First milestone in implementation of the EU AI Act (timeline)).

Key implications for healthcare: the AI Act creates risk tiers (prohibiting social‑scoring and certain biometric scraping), imposes strict requirements on high‑risk systems (data governance, documentation, human oversight) and requires Member States to designate national competent authorities by early August 2025 - a structure Iceland will mirror through the EEA route.

Iceland's existing AI policy and data rules (GDPR implementation, emphasis on human‑rights, and recommended DPIAs for personal‑data training) mean hospitals and labs should be preparing governance, impact assessments and vendor due diligence now so AI pilots can meet the incoming EEA adoption without costly rework.

Data protection and patient privacy are non‑negotiable guardrails for AI in Icelandic healthcare: GDPR applies in Iceland through Act No. 90/2018 (the Data Protection and Processing Act), meaning hospitals, labs and digital‑health vendors must build AI projects on documented legal bases, strong security, and clear accountability rather than hopeful assumptions - see the Iceland country guide for the implementing law (Act No. 90/2018 Data Protection and Processing Act overview (Iceland GDPR implementation)) and practical enforcement guidance (Persónuvernd enforcement and Iceland data protection practical rules).

“so what?”

The “so what?” is stark: weak data governance can convert an otherwise useful AI pilot into costly fines, remediation and reputational damage - Iceland's framework rewards careful design, clear consent/legality, and privacy‑first engineering.

For Icelandic healthcare teams, the legal landscape for AI is less a mystery than a checklist: there's no standalone domestic AI law yet - policy leans on the national AI strategy and on EEA adoption of the EU AI Act - so hospitals and vendors must marry existing rules on data, safety and IP with incoming EEA obligations; start with the fundamentals - GDPR through Act No.

90/2018 for patient data, the Icelandic Patents Act No. 17/1991 for inventions (software can be patentable when it has a technical effect), and the Trade Secrets Act No.

131/2020 to protect confidential algorithms and training datasets. Expect copyright gaps (the Copyright Act No. 73/1972 is being modernised to catch up with AI) and new criminal protections for deepfake sexual‑privacy abuses, while the nation's small scale shows up in the numbers - a single reported AI patent in 2023 - making collaboration, clear ownership and IP financing crucial; recent international work on IP and innovation financing discussed at the EPO's Reykjavik meeting underscores the practical need to translate research into investable assets (Iceland AI & IP legal guide, EPO meeting on innovation financing in Reykjavík).

Plan for DPIAs, solid vendor contracts, trade‑secret hygiene and a patent strategy that prioritises technical contributions to keep AI pilots compliant and investable.

In Iceland, AI and deep learning are shifting from emergency tools into everyday clinical scaffolding: population‑scale genomic sequencing and app‑based surveillance proved their value during COVID‑19 (DeCode sequenced every new case, often within 2–3 days), telemedicine plus EMR triage (green/yellow/red) kept patients safe at home, and multilingual patient‑facing chatbots can shave call‑centre load and speed access to care (Study: COVID‑19 and AI in Iceland (OAEPublish), see Rakning C‑19 and telemedicine sections).

Those strengths - high‑quality national registries with validated chronic‑disease diagnoses and an unusually dense testing footprint (almost a quarter‑million domestic tests in 2020) - create fertile ground for reliable supervised and multimodal models, while European guidance and new validation tools underscore the need to measure bias, uncertainty and clinical performance before deployment (emerging methods such as MIGHT improve AI reliability in diagnostics).

Practically, Icelandic hospitals can expect AI to speed imaging and genomics interpretation, predict admissions and resource needs, automate routine admin work, and support targeted public‑health action - but success requires good training data, transparent validation and human oversight so models augment clinicians without introducing unchecked risk (Patient‑facing healthcare chatbot example in Iceland (case study), Johns Hopkins: new method advances AI reliability in medical diagnostics).

Start small, govern tightly and scale only after repeatable wins: Icelandic providers should align pilots with the national AI strategy and action plans captured in Iceland's OECD AI Policy Observatory country profile for Iceland while embedding a clear governance cycle - use‑case selection, data stewardship, validation, deployment and continuous monitoring - so models don't outpace oversight.

Prioritise high‑value, low‑risk pilots that prove operational benefit (for example multilingual patient‑facing chatbots that handle appointments, lab results and Rakning C‑19 guidance to shave call‑centre load) and pair each pilot with robust health‑data governance and interoperability plans inspired by the WHO ethics and governance of AI for health guidance on health data governance.

Build a multidisciplinary team (clinicians, IT, legal, ethics and procurement), require vendor due diligence and transparent documentation, and adopt lifecycle controls for explainability, bias testing and human oversight as recommended in contemporary AI governance frameworks; these steps turn technical experiments into compliant, investable services.

A memorable rule of thumb: if an AI pilot can't show how it improves a single clinician or patient workflow in one quarter, pause and rework the data and governance first - practical checkpoints win public trust and make national rollout possible.

Iceland's COVID‑19 experience is a practical case study in what works - and what doesn't - when tech meets public health: the government's Rakning C‑19 app achieved an impressive 38% penetration (the highest per‑capita download rate reported), but investigators quickly learned that automated tracing only adds value when tightly welded to skilled manual contact‑tracing and follow‑up (MIT Technology Review analysis of the Rakning C‑19 contact tracing app); as Detective Inspector Gestur Pálmason put it, the app “proved useful in a few cases, but it wasn't a game‑changer.” At the same time, Iceland's combination of mass testing (almost 240,000 domestic tests in 2020), rapid genomic sequencing by DeCode (usually within 2–3 days) and a telemedicine hub that triaged patients into green/yellow/red categories turned data into targeted action rather than blunt lockdowns (Directorate of Health review of COVID‑19 testing and AI use in Iceland); the vivid lesson is simple and sharp - digital tools can amplify a small system's agility, but only if citizens trust how data are used, and that trust is fragile when privacy policies are hard to read or permissions feel excessive, a problem flagged in broader app‑policy readability research.

“The technology is more or less … I wouldn't say useless… I would say it [Rakning] has proven useful in a few cases, but it wasn't a game changer for us.”

The clearest next steps for AI in Icelandic healthcare are practical and already mapped out: accelerate Landspítali's Science Policy by converting its annual Science Fund (over 100 million ISK) and the target of science comprising 3% of hospital turnover into concrete hires, postdoctoral positions and better data infrastructure (electronic data centre and biobank) so clinicians have “defined time” to lead research and validated pilots, tighten information‑security and compliance by building on Landspítali's Information Security Policy and ISO‑27001 commitments, and pair each pilot with robust DPIAs, vendor due diligence and public‑facing communication to preserve trust.

Prioritise small, measurable pilots that improve a single clinician or patient workflow within a quarter - examples include multilingual patient chatbots and streamlined genomics pipelines that proved their value during COVID‑19 - and expand only after repeatable results and rigorous validation.

Finally, shore up skills across clinical, legal and IT teams so staff can operationalise AI responsibly; short, practical courses (for example, the 15‑week AI Essentials for Work program) can equip non‑technical staff to use AI tools, write effective prompts and manage vendor relationships, turning policy ambitions into safe, scalable services that respect privacy and raise Iceland's scientific profile (Landspítali Science Policy 2025–2030, AI Essentials for Work - 15‑Week bootcamp (Nucamp registration)).