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

Turkey's AI-in-healthcare landscape in 2025 is energetic and pragmatic: the Turkey generative AI market already reached USD 128.16 million in 2024 and is projected to grow to USD 546.31 million by 2033, driven by localization for Turkish language use and rising enterprise adoption (Turkey generative AI market report); yet clinicians urge caution, noting clear gaps in training, regulation, and ethical safeguards that must be closed before scale (survey of Turkish medical oncologists' perspectives on AI).

Turkey's market sits ahead of its MENA peers - so far advanced that BCC Research excluded it from a MENA regional analysis - making local policy, datasets, and hospital workflows the real battlegrounds for safe adoption (BCC Research MENA AI in Healthcare analysis).

For healthcare professionals and managers, practical upskilling (for example, Nucamp AI Essentials for Work 15-week bootcamp) and attention to localization, governance, and procurement will determine whether AI eases pressure on clinics or adds another layer of risk.

Turkey is excluded from the report due to its being significantly more advanced AI in the healthcare market than other MENA countries.

Looking beyond policy debates, concrete deployments show where AI already adds value for hospitals and what Turkey can learn: Acıbadem Healthcare Group's Istanbul hub installed 900 Hanwha Vision cameras to pair AI-powered video analytics with patient-flow and safety goals - occupancy monitoring, queue management, lost‑patient search and perimeter security - that directly inform staffing, cleaning schedules and waiting‑room design (Hanwha Vision AI video system case study for Turkish hospital); the vivid takeaway is simple and practical - real‑time sensors can turn a crowded waiting room into actionable data for a nurse's shift roster.

Lessons from nearby infrastructure projects also matter: Turkish traffic systems upgraded with modern AI accelerators achieved 50% lower inference latency and 33% lower construction costs, a reminder that hardware and edge compute choices materially affect speed, cost and scalability for real‑time clinical uses (Advantech AI-assisted traffic surveillance case study in Turkey).

For hospitals and healthtech startups, the practical path is visible - start with focused, measurable pilots (patient flow, remote monitoring, trial matching), use proven edge/cloud stacks, and translate analytics into staffing and procurement decisions rather than hypothetical accuracy gains (Remote monitoring and healthcare AI use cases in Turkey).

Turkey's 2025 AI horizon is pragmatic: scale the “must-haves” that relieve daily pressure (AI‑assisted radiology, predictive hospital analytics, virtual health assistants) while piloting higher‑impact bets such as digital twins and autonomous agents under strict clinical oversight; a useful roadmap comes from 2025 trend analyses that separate game‑changers from hype and urge leaders to prioritize proven operational wins (2025 AI in Healthcare Trend Radar report).

Local voices reinforce caution - physicians, nurses and patients in Turkey flag training, explainability and governance as prerequisites for safe rollout, not afterthoughts (Study: perspectives of physicians, nurses, and patients on AI and robotic nurses).

Clinical studies echo the same safeguard: AI can match or exceed human performance on knowledge tasks but underperforms on case‑based clinical judgement, so tools should augment clinicians rather than replace them (Comparative study of orthopedic surgeons vs AI in spinal surgery (JTSS 2025)).

The practical takeaway for Turkish hospitals and startups is concrete: deploy sensors, IoMT and AI where they measurably cut waits and staffing strain, run tightly scoped pilots for high‑risk clinical models, invest in clinician upskilling and data governance, and treat AI as a learning system that must prove equity and safety in Türkiye's heterogeneous patient population - a single well‑tuned predictive model that prevents one ICU transfer is worth more than ten unvalidated demos.

Türkiye's AI policy in 2025 sits between active preparation and important gaps: a Draft Artificial Intelligence Law modelled on the EU's risk‑based approach was submitted to the Grand National Assembly in June 2024 and would set broad principles - safety, transparency, fairness, accountability and privacy - while creating roles for providers, implementors/users, importers and distributors (collectively operators); however, observers note the draft is thinner than the EU AI Act on risk‑tiering, enforcement details and a designated supervisory authority, and Turkey continues to rely on sectoral rules and KVKK guidance for data protection in AI systems.

The practical takeaway for hospitals and startups: compliance will mean navigating parallel regimes - national principles and non‑binding KVKK guidance domestically, plus the EU AI Act's extraterritorial Brussels effect for services reaching EU patients - and preparing for hard penalties (e.g., prohibited uses can carry large fines), registration and conformity checks for high‑risk systems as the law and secondary rules are finalised; the most vivid risk is simple and concrete: one unregistered high‑risk clinical model could trigger multimillion‑TL fines or forced market withdrawal, so legal alignment and data governance are non‑negotiable.

For background on EU parallels and Turkey's evolving framework, see the Draft AI Law overview in the AI Policy Corner and White & Case's Turkey tracker and the Montreal AI Ethics Institute's policy brief.

operators

Brussels effect

Türkiye's national AI programme is anchored in the 2021–2025 National Artificial Intelligence Strategy (NAIS), crafted by the Digital Transformation Office (DTO) and the Ministry of Industry and Technology to build “an agile and sustainable AI ecosystem” across six strategic priorities - skills, research & startups, data & infrastructure, regulatory adaptation, international cooperation and workforce transformation - and a dense roadmap of 24 objectives and 119 measures that aim for concrete outcomes by 2025; the plan even sets numeric targets (raise AI's GDP contribution to 5%, grow AI employment to 50,000 and increase graduate AI diplomas to 10,000) and allocates coordinated governance across a two‑layered mechanism under DTO and MoIT (see the NAIS overview).

Implementation is equally practical: a Public AI Ecosystem, TÜBİTAK‑hosted sectoral co‑creation labs and a Public Sector Data Space are designed to let public and private teams develop and test models using secure, privacy‑preserving data access instead of wholesale data transfers, and DTO has been running quarterly monitoring and short‑term action plans while piloting pieces such as a Turkish LLM, a “trustworthy AI” seal and algorithmic accountability guidance (progress metrics and programme details are summarised by OECD and recent DTO updates).

For hospitals and health startups the upshot is clear - national support is real, governance is tightening, and the infrastructure to run validated, locally‑relevant pilots is now being stood up.

generate value on a global scale with an agile and sustainable AI ecosystem for a prosperous Turkey

Turkey's healthcare AI ecosystem already centers on a handful of institutions and platforms that matter for any hospital or startup planning a pilot: Ondokuz Mayıs University (OMU) is leading a high‑visibility effort - HealthGPT, described as Türkiye's first large language model for healthcare and to be trained explicitly with Turkish medical data - anchoring academic work to institutional systems like UBYS, AVESİS and OMU's software repository (Ondokuz Mayıs University HealthGPT announcement – Türkiye's first AI-powered healthcare model); national funder TÜBİTAK remains the backbone for scale through grant programmes and international partnerships (notably a 2025 TÜBİTAK–NSFC joint call for medical AI, imaging and translational oncology projects) that open routes to multicenter datasets and validation cohorts (TÜBİTAK–NSFC 2025 joint call for medical AI and imaging projects).

Practical tooling and data hygiene are also visible: vendors and start‑ups have TÜBİTAK‑backed successes (CranioCatch in AI dentistry) and platforms such as Tiga's Autononym for robust anonymization, while systems integrators like NorthBay Solutions are already proving how a Turkish LLM can be trained and deployed on cloud infrastructure for faster iteration (NorthBay Solutions case study: TÜBİTAK Turkish LLM deployment on AWS).

The practical lesson is clear and memorable: combine trusted funders, university datasets, strong anonymization tools and cloud partners - one well‑governed Turkish LLM can turn scattered hospital records into an actionable clinical assistant that speaks the language of local clinicians and patients.

"The goal of this cooperation is to support projects that contribute to sustainable development and public health and that are at a high level of scientific and technological excellence."

Turkey already shows a mix of mature, low‑risk deployments and fast‑moving clinical pilots: at Acıbadem Ataşehir, 900 Hanwha Vision cameras pair AI video analytics with people‑counting, occupancy and queue‑management so hospital managers can turn a crowded waiting room into staffing and cleaning plans in real time (Hanwha Vision AI video analytics hospital case study); radiology is another clear sweet spot, with new national hubs - like Marsirius AI Labs' duAIcheck platform and trial portal - bringing curated AI tools for cardiology, breast and lung lesion detection into integrated PACS workflows for real‑time second reads and trial validation (Marsirius duAIcheck radiology AI platform and trial portal partnership).

Peer‑review evidence from emergency medicine underlines why imaging and triage are practical first bets: AI imaging systems routinely report 85–90% accuracy in X‑ray and CT tasks, and NLP/LLMs are proving useful for triage and outcome prediction when carefully validated (Scoping review on AI in emergency imaging and triage - Turk Journal of Emergency Medicine).

Emerging opportunities - remote monitoring and wearables for chronic care, AI‑assisted clinical trial matching, and locally trained LLMs that speak Turkish - are within reach, but the operational lesson is blunt: choose pilots with measurable operational KPIs (reduced wait time, faster reads, improved enrollment), validate across Turkish cohorts, and lock down anonymization and workflow integration before scaling.

“Leveraging the power of GE Healthcare's Edison Health Services, Turkish clinical scientists, data scientists and software developers will be able to harness our immense datasets to create AI-driven applications that can add significant value to the diagnosis and treatment of diseases in the Turkey.”

For hospitals and healthtech teams in Türkiye, data governance for AI starts with the KVKK basics: health data is a

special category

that generally requires explicit consent or a narrowly applied legal exception, and controllers must follow principles of purpose limitation, data minimization and security when collecting, storing or using patient records for model training (KVKK compliance guide for Türkiye data protection law).

Practical must‑haves include registering as a data controller in the public VERBİS registry when required (Article 16), documenting lawful bases and retention limits, and embedding strong technical and organisational safeguards so that breaches are reported quickly - the law expects notification to the Data Protection Board and affected individuals when risk is high, typically within 72 hours.

Cross‑border AI work must respect Article 9 rules: export of Turkish patient data normally needs explicit consent or Board approval unless the receiving country offers an adequacy decision or contractual safeguards.

Regulators and lawyers note that KVKK obligations sit alongside Turkey's emerging AI framework, so teams should bake in anonymization/pseudonymization, DPIA‑style risk assessments for clinical models, robust audit trails and user‑facing transparency; failure to get this right can mean regulatory probes and administrative fines under KVKK as well as sectoral scrutiny described in Turkey AI trackers (White & Case AI regulatory tracker for Turkey).

Standards, certification, liability and procurement for AI medical systems in Türkiye are built on a clear EU‑aligned device framework: manufacturers must plan for Medical Device Regulation/IVDR conformance (CE certificates, UDI and post‑market surveillance) and complete TITCK document registration before devices enter the market, so procurement teams should treat conformity evidence and an ISO 13485‑style QMS as table stakes TITCK guidance on medical device supply interruptions and shortages, analysis of TITCK harmonisation with EU MDR and IVDR.

Commercial contracts therefore need explicit early‑warning clauses and clear Turkish‑based authorised‑representative responsibilities because the law now requires economic operators to notify TITCK at least six months before any foreseeable shortage or discontinuation - a single missed notification can force accelerated import measures or market‑withdrawal steps under Law No.

7223 once the transition period ends. Procurement must also check distribution and sales‑center qualifications, training certificates and GDP/compliance readiness (TITCK inspection guidance), and build contractual audit rights and evidence trails for UDI, vigilance and accelerated‑import safeguards; in short, buying an AI medical system in Türkiye is as much about supplier governance and regulatory proof as about algorithm performance, anchored by the six‑month early‑warning clock and the authority's growing enforcement toolkit.

Start small, measure fast, and keep patient care front and center: a practical roadmap for Turkish hospitals and health startups begins with operational pilots that show clear KPIs - inventory, patient flow and scheduling, and triage/radiology second‑reads are all high‑value first bets - then scale once integration, data quality and staff training are proven.

Choose partners with local experience and proven outcomes (for Istanbul supply chains, platforms like Autonoly advertise dramatic time‑savings and ROI with workflows tuned to local logistics) Autonoly Istanbul medical supply chain management case study, and prefer solutions that plug into ERP/EHR and automate replenishment so clinicians reclaim time.

Real deployments show what matters in practice: autonomous inventory systems have delivered step‑change results - 95% fewer stockouts and up to 47% less waste in live hospital rollouts - so include hard operational targets (stockouts avoided, clinician hours saved, days‑to‑ROI) in any pilot plan and validate models on Turkish cohorts before broad rollout (Chooch AI healthcare supply chain case study).

Protect data and adoption with phased change management: clean and integrate masters, train frontline staff, codify workflows, and document compliance with Turkish privacy and procurement rules; for teams new to applied AI, short practical upskilling - such as the AI Essentials for Work bootcamp - helps translate pilots into lasting improvements Nucamp AI Essentials for Work bootcamp registration.

“It's like going from a paper map to GPS. Supplies are just there.”