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

Kazakhstan's 2025 AI push is already reshaping healthcare: a newly created "digital headquarters" is driving AI into public services and hospitals to improve diagnostics, personalise treatment plans and enable continuous patient monitoring while giving startups access to ministry infrastructure and a unified medical database (Astana Times: Kazakhstan creates digital headquarters to facilitate AI integration).

At the same time the state has approved an AI development concept through 2029 and launched national assets such as alem.cloud and AlemLLM to host local models and compute at scale (Astana Times: Kazakhstan accelerates digital transformation with AI, blockchain, and global tech ambitions).

Practical workforce training matters: programs for clinicians and managers must catch up, and short, applied courses like Nucamp's AI Essentials for Work (15 weeks) teach nontechnical staff how to use AI tools and write effective prompts for real-world clinical and administrative tasks (Nucamp AI Essentials for Work syllabus - 15-week practical AI course for workplace).

Bootcamp	Length	Early-bird Cost	Registration
AI Essentials for Work	15 Weeks	$3,582	Register for AI Essentials for Work bootcamp
Solo AI Tech Entrepreneur	30 Weeks	$4,776	Register for Solo AI Tech Entrepreneur bootcamp

“It is not only about improving the legal framework for the functioning of AI. It is necessary to address matters of data fragmentation, the lack of clear regulations for the distribution of supercomputer capacity, cybersecurity, and the complete transition to the QazTech platform. All these tasks are of strategic importance for the country's digital transformation and must be completed by December of this year with a visible economic effect,” said Bektenov.

What does AI actually do in Kazakhstan's clinics? Practically speaking it turns imaging and routine data into faster, repeatable decisions: systems like PneumoNet use machine learning to flag abnormalities on CT scans, triage patients and speed up radiologists' workflows so scarce specialists can focus on the hardest cases; today PneumoNet is connected to hundreds of devices and performs tens of thousands of screenings monthly, a volume equivalent to screening a small city every month (World Bank feature: PneumoNet rapid CT‑screening rollout in Kazakhstan).

The immediate practical basics for providers are straightforward: deploy AI where routine image-reading and triage create bottlenecks, ensure data pipelines feed models with clean, consented records, and pair tools with staff training so clinicians trust and can act on AI outputs - a need underscored by WHO‑led calls to build digital health literacy and interdisciplinary curricula (WHO report: digital health workforce training in Kazakhstan).

Regulation and infrastructure shape implementation: draft national rules propose a risk‑based approach for high‑impact systems and a centralized AI platform to host models and data, so hospitals should plan governance and human oversight as part of any rollout.

The “so what?” is concrete: when routine reads are automated, wait times shrink and clinicians can treat more urgent patients sooner - a visible, measurable relief for overburdened radiology units.

Metric	Value
Diseases PneumoNet can identify	17 infectious lung diseases
Connected medical devices	240
Medical organizations / polyclinics	130
Monthly screenings	~30,000
CT scans per day (pandemic surge)	from ~60 to ~100

“In the early days of the pandemic, frontline medical staff were introduced to working with the PneumoNet system. By May 2020, the system was used by three frontline hospitals in Almaty and Nur-Sultan, allowing radiologists to do their work in half the time and expediting the triaging of patients based on need for critical care and hospitalization. In addition, the system complemented the PCR diagnoses as the number of COVID-19 cases increased,” says Dauren Baibazarov, the executive director of Forus Data.

Looking ahead to 2025 and beyond, Kazakhstan's healthcare future will blend practical pilots with national-scale infrastructure: hospital trials of an “AI therapist” that can make preliminary diagnoses with up to 80% accuracy signal a move from research prototypes to bedside tools (Kursiv report: AI therapist hospital trials in Kazakhstan (80% diagnostic accuracy)), while the country's push to host local models and compute - AlemLLM, an international AI centre and a new supercomputer cluster - creates the secure, sovereign platform needed for clinical-grade systems to scale (Astana Times analysis: AlemLLM, national AI platform and supercomputer cluster in Kazakhstan).

The practical path forward is clear: couple high‑impact pilots with robust governance, invest in clinician training and interdisciplinary curricula, and use centralized compute and data platforms to keep models localized, auditable and privacy-aware; when those pieces click, expect faster triage, wider telemedicine reach and measurable relief for overloaded clinics - but only if workforce development and regulation keep pace.

“The planning horizon for education and human resource management in health care is extremely long, while technologies are bringing us everyday revolutions right now. This means we have to address the topic of digital health in pre-service education in a strategic and mindful manner,” stated Mr Beibut Yessenbayev, Vice Minister of Health of Kazakhstan.

Kazakhstan's emerging national AI strategy is reshaping how healthcare AI will be governed in practice: the Mazhilis approved a draft law on artificial intelligence in its first reading that embeds human‑centric principles - legality, fairness, transparency, explainability and accountability - and explicitly treats systems that affect life and health as high‑risk, meaning hospitals and vendors must plan for stricter oversight, clearer data protections and human-in-the-loop safeguards (Astana Times article on Kazakhstan draft AI law).

The bill proposes banning fully autonomous decision‑makers, introduces limits on unauthorized data collection (with possible criminal liability for mass misuse), and envisions a National AI Platform as a unified infrastructure for hosting models, compute and training data - a setup that could speed safe, auditable deployments of radiology and triage tools while forcing new compliance workstreams for clinical teams (Silkway TV report on National AI Platform).

The practical consequence for health providers is straightforward and immediate: classify clinical systems as high‑impact from the outset, embed governance and explainability into procurement, and budget for staff retraining and data‑security measures so AI augments care rather than adding legal or operational risk - imagine a clinic where every algorithmic recommendation arrives with a clear provenance trail and a named clinician responsible for the final decision.

Provision	What it means for healthcare AI
Human‑centric principles	Transparency, explainability and accountability required for clinical systems
Risk‑based classification	Healthcare systems classed as high‑risk → stricter controls and oversight
Ban on fully autonomous systems	No deployment of AI that makes decisions without human oversight
National AI Platform	Centralized hosting for models, data libraries and testing environments

“The bill reflects major global trends in AI regulation. Many countries have adopted systematic approaches to AI governance. The EU's AI Act, adopted in 2024, serves as the world's first risk-based AI legislation and is already a model for countries like Kazakhstan,” said Sholpan Saimova.

Infrastructure and national data programs are quietly doing the heavy lifting behind Kazakhstan's healthcare AI: the Smart Data Ukimet (SDU) pipeline already pulls together 93 state databases - from ministries and banks to universities - creating the centralized, consented feeds clinical models need, while the planned National AI Platform promises shared datasets, pre‑trained models and on‑demand compute for hospitals and startups (Smart Data Ukimet national data pipeline and Kazakhstan National AI Platform concept).

At the same time, a national supercomputer sited at Alem.AI, built with Presight and NVIDIA H200 clusters, brings massive GPU capacity (up to two exaflops in FP8) to train large medical models and perform secure on‑premise inference for sensitive patient data (Alem.AI national supercomputer launch with Presight and NVIDIA H200).

Regulatory and infrastructure roadmaps are aligned with governance work - risk tiers, data limits and a centralized hosting model - to keep compute local and auditable (Kazakhstan AI regulation overview and National AI Platform governance).

The result is tangible: clinical pilots can tap a sovereign pipeline and supercompute instead of shipping records abroad, turning a fragmented health data landscape into a single, secure lifeline for diagnostics and research - picture hundreds of hospital CT feeds flowing like veins into a single, governed model that flags the sickest patients first.

Program / Metric	Value / Note
Smart Data Ukimet - connected databases	93 state databases (gov, quasi‑public, banks, universities)
National supercomputer	Up to 2 exaflops (NVIDIA H200, FP8) at Alem.AI
Kazakh‑language AI model	Trained on 148 billion tokens (Dec 2024)
National AI Platform	Centralized datasets, compute, pre‑trained models for agencies, startups, universities

“Kazakhstan has been very active in developing its domestic digital infrastructure, developing the talent and the use cases, particularly in the area of government services.” - Amandeep Singh Gill

Building the human layer under Kazakhstan's AI ambitions is now a national project: AI has been made a compulsory discipline across universities, with 93 institutions already integrating courses and 20 institutes launching 25 new tracks to feed hospitals, labs and startups with trained talent (Artificial intelligence made mandatory in Kazakh universities - Astana Times), while the government's 2024–2029 AI Concept ties academic reform to real clinical needs - use of the national supercomputer and shared datasets for medical models, plus targets to scale trained specialists and industry-ready graduates (Kazakhstan AI Development Concept 2024–2029 - PrimeMinister.kz).

School pilots and creative tech centres add depth to the pipeline: the “Day of AI” lessons for grades 1–4 and planned TUMO centres aim to seed digital fluency early, while over 390,000 students have already completed foundational AI courses and some 3,000 teachers earned certificates - a volume that can turn radiology and telemedicine pilots into sustainable services because clinicians and technicians will finally have local training pathways and computing resources to iterate models on real Kazakh data (Kazakhstan to teach AI in schools and launch TUMO centres - IntelligentEdu).

The memorable payoff: when classrooms, bootcamps and national compute feed the same talent pipeline, a newly trained nurse or radiographer can move from an introductory AI lesson to operating a clinical triage model in months, not years - shortening the path from education to safer patient care.

Metric	Value
Universities integrating AI	93
New AI tracks launched	25 (across 20 institutions)
Students completing foundational AI courses	~390,000
Teachers certified in AI	~3,000
TUMO centre annual training capacity	~10,000 students (projected)

“Every student will be able to learn how to apply AI in their profession, develop new technologies, or create start-ups in the future,” said Deputy Minister Gulzat Kobenova.

PneumoNet is a clear, home‑grown case study of how AI can move from lab to ward in Kazakhstan: developed by the Kazakh Research Institute of Oncology and Radiology with Forus Data, the system rapidly triages CTs to identify 17 infectious lung diseases (including pneumonia, tuberculosis, cancer and COVID‑19), and by May 2020 was already deployed in three frontline hospitals in Almaty and Nur‑Sultan - helping radiologists halve their reading time and prioritise patients who need critical care (World Bank feature: PneumoNet AI triage system in Kazakhstan).

Today the platform links roughly 240 medical devices across 130 organisations and performs about 30,000 screenings monthly - effectively screening a small city every month - and the Technology Consortia Grant Program's $340,000 award helped turn the prototype into a nationwide service while seeding follow‑on pilots such as LungCancerCT at the Almaty Oncological Center and planned MGraphNet trials for breast screening (Borgen Project: Health care in Kazakhstan and PneumoNet overview).

The practical lesson for hospitals: pair targeted funding and centralised pipelines with clinician training and governance, and an AI screening tool can cut backlog, speed referrals and extend diagnostic reach into rural clinics without exporting sensitive records abroad.

Metric	Value
Diseases PneumoNet can identify	17 infectious lung diseases
Connected medical devices	240
Medical organisations / polyclinics	130
Monthly screenings	~30,000
Initial frontline hospitals (May 2020)	3 (Almaty, Nur‑Sultan)
TCGP grant	$340,000

“In the early days of the pandemic, frontline medical staff were introduced to working with the PneumoNet system. By May 2020, the system was used by three frontline hospitals in Almaty and Nur-Sultan, allowing radiologists to do their work in half the time and expediting the triaging of patients based on need for critical care and hospitalization. In addition, the system complemented the PCR diagnoses as the number of COVID-19 cases increased,” says Dauren Baibazarov, the executive director of Forus Data.

Global leaders set the pace - and Kazakhstan is plotting a practical route to join them. The United States still dominates private AI investment (roughly $109.1 billion in 2024), driving a rich ecosystem of clinical models and device approvals, while China's state‑backed, data‑rich market is racing ahead in APAC with huge imaging and telemedicine deployments (Stanford HAI 2025 AI Index report; China Briefing: China's AI healthcare market growth and trends).

Other innovators - NHS pilots in the U.K., Israel's startup cluster, and Singapore's government LLMs - show that different combinations of funding, regulation and data infrastructure produce impact in diagnostic accuracy and access.

For Kazakhstan, the sensible takeaway is not to imitate any single model but to combine sovereign data platforms, targeted pilots (like national imaging screening) and workforce training so that limited budgets buy durable gains; think of provincial hospitals becoming hubs rather than islands, where algorithmic triage routes urgent cases to scarce specialists.

That middle‑income playbook echoes the Government AI Readiness finding that countries beyond the high‑income leaders are closing the gap by getting governance, data and education right - precisely the levers Kazakhstan is tightening now (Government AI Readiness Index (Oxford Insights)).

Country / Group	Notable strength
United States	Largest private AI investment → rapid clinical model and device development
China	State-backed scale, centralized health data and fast commercialization in imaging & telemedicine
Kazakhstan	National data platforms, sovereign compute and targeted pilots position it to scale safe, localized healthcare AI

Practical adoption starts with a clear, doable playbook: first, treat clinical AI as a risk‑tiered technology - assess whether a tool is “high‑risk” (anything that can affect life or health) and plan stricter validation, documentation and human‑in‑the‑loop controls accordingly, in line with Kazakhstan's emerging regulatory framework (AI regulation in Kazakhstan overview); second, lock down data flows and consent - biometric and other sensitive records require explicit consent under existing personal‑data rules, so pipeline design must enforce provenance and retention limits; third, prefer sovereign hosting and audited compute via the National AI Platform and the country's single big‑data space to keep inference and model training local and auditable (Kazakhstan National AI Platform and Big Data (eGov.kz)); fourth, budget for governance, explainability and liability (a draft law even contemplates bans on fully autonomous systems), and build human oversight into procurement so every algorithmic recommendation arrives with a named clinician to review it; finally, invest in practical training - use simulated local cases and short applied courses such as the Tech Orda training simulator to get clinicians and technicians operational fast (Tech Orda AI training simulator for Kazakh clinicians).

The “so what?” is simple: with risk classification, consented data, sovereign compute and rapid staff upskilling, a provincial hospital can safely run an auditable triage model that flags the sickest patients first - shortening waits and protecting patient privacy at the same time.

Step	What to do
Risk classification	Assess systems against the draft risk tiers; treat clinical tools as high‑risk and require stricter oversight
Data protection & consent	Obtain explicit consent for biometric data and comply with personal data laws
National hosting & big data	Use the National AI Platform and single big‑data space to keep models and data local and auditable
Human oversight & training	Build clinician review into workflows and use local simulators/short courses to train staff (nearly 2,000 civil servants have received AI training)

Kazakhstan's healthcare AI story in 2025 closes this guide on a cautiously optimistic note: the draft law “On Artificial Intelligence” offers a clear regulatory hinge to turn pilots into safe, scalable services - so long as it is harmonised with the broader Digital Code and paired with practical implementation steps (Kazakhstan draft law "On Artificial Intelligence" (official text)).

The upside is concrete - sovereign platforms, state-led compute and targeted funding can keep sensitive patient data local and let clinicians iterate models on real Kazakh workflows - but real risks demand urgent attention, from skills shortages to cybersecurity (a June leak exposed roughly 16.3 million records, a vivid reminder that infrastructure and oversight must be built first) (Kazakhstan 16.3 million-record data breach and AI rollout cybersecurity concerns).

Practical next steps for hospitals and ministries are straightforward: adopt the draft's risk‑based governance, invest in rapid, applied retraining for clinical and administrative staff, and use sovereign compute with constant auditing; short, work-focused courses - such as Nucamp AI Essentials for Work bootcamp (15 weeks) - can quickly upskill nontechnical staff to operate and oversee clinical AI safely.

If regulation, infrastructure and human capital advance together, Kazakhstan can translate national ambition into faster diagnoses, fairer access and measurable health gains - without trading away privacy or control.

Bootcamp	Length	Early‑bird Cost	Registration
AI Essentials for Work	15 Weeks	$3,582	Register for Nucamp AI Essentials for Work bootcamp

“I have already spoken about accelerating the creation of a unified national digital ecosystem,” Tokayev said.