The Complete Guide to Using AI in the Healthcare Industry in Czech Republic in 2025

Introduction: The Complete Guide to Using AI in the Healthcare Industry in the Czech Republic (2025) - The Czech Republic in 2025 is building real momentum for healthcare AI: the National AI Strategy 2030 and EU AI Act roll‑out are driving policy and testing environments (national sandboxes and TEFs including a healthcare TEF), while targeted funding programmes - from TWIST to an OP TAK call that opened up to CZK 1.5 billion - have fuelled startups such as Aireen (diabetic retinopathy), Kardi Ai and Carebot, which together have attracted multi‑million investments and clinical certification wins; a Deep Tech call was even 200% oversubscribed, showing high demand but also pressure on capacity.

Regulatory clarity, export restrictions on advanced AI chips (Jan 13, 2025) and evolving GDPR practice mean hospitals and innovators must pair technical pilots with governance; see the legal and funding context at Global Legal Insights and the NAIS 2030 overview, and consider practical upskilling via Nucamp's AI Essentials for Work.

“These updates reflect the dynamic environment businesses and organisations will navigate in 2025, presenting both challenges and opportunities.” - Miroslav Dubovský

AI in Czech healthcare is not a distant idea but a practical accelerator: from radiology tools that scan thousands of images in minutes to generative systems that cut the administrative load so clinicians can spend more time with patients.

Czech experience shows concrete gains - AI-driven screening for diabetic retinopathy can be done in a diabetologist's office, widening access and catching sight‑threatening disease earlier, while hospital pilots use AI to triage imaging and predict acute deterioration; these are the same capabilities that are reshaping radiology, genomics and telemedicine across Europe.

Successful adoption in the Czech Republic depends on good data, clear governance and clinician oversight so AI augments rather than replaces judgment - a theme underlined in national guidance and practical hospital toolkits.

For hospitals planning pilots, start with image‑based diagnostics and administrative automation where evidence is strongest, pair each pilot with rigorous validation and staff training, and consult country‑specific guidance such as the ČAUI handbook for hospitals and academic analyses on radiology and diagnostics to design safe, scalable pilots.

See more on radiology and diagnostics and the practical hospital guide for clinicians.

„Umělá inteligence v radiologii už je a určitě zde zůstane. Dokáže analyzovat obrovské množství snímků rychleji než radiolog. Vývoj zatím ukazuje, že nenahradí radiology, ale poskytne jim nástroje pro podporu jejich rozhodování.“ - doc. Andrea Burgetová, Radiodiagnostická klinika 1. LF UK a VFN

The near-term future of AI in Czech healthcare looks like a pragmatic sprint rather than a distant marathon: with the National AI Strategy 2030 already in place and testing environments and European Digital Innovation Hubs scaling up, Czech hospitals and startups can pilot real tools under clearer national direction (see the NAIS 2030 overview).

2025 brings concrete regulatory pressure too - the EU AI Act's first bans and obligations come into force this year, forcing vendors and buyers to build compliance into products from day one (read the 2025 Czech legal landscape).

Market demand is visible: specialised AI medical‑diagnosis apps are forecast to expand in the coming years, while workforce studies warn that generative AI could affect over four in 10 Czech jobs - roughly 2.3 million workers - making retraining and role redesign essential (see the study on jobs affected by generative AI).

Practically, expect growth where evidence and regulation align: diagnostic imaging, predictive operations, virtual assistants and clinician workflow tools that cut admin time - and even the “AI helper app” that watches for missed tests 24/7 - will scale fastest because they deliver measurable ROI and patient‑safety gains.

The takeaway for Czech providers: pair pilots with governance, invest in upskilling, and prioritise solutions that are both clinically validated and AI‑Act ready to move from promising trial to everyday use.

“These updates reflect the dynamic environment businesses and organisations will navigate in 2025, presenting both challenges and opportunities.” - Miroslav Dubovský

The National AI Strategy 2030 (NAIS) is the Czech Republic's practical roadmap for turning AI from research promise into everyday advantage: approved in an updated form on 24 July 2024, it ties into the Digital Czech Republic programme and the Government Innovation Strategy 2019–30 and lays out seven interconnected priority areas - from research, education and labour‑market skills to legal/ethical, security, business and public‑service uses - so hospitals and health tech startups know where public support and oversight will concentrate.

NAIS emphasises centres of excellence, testing environments and a network of European Digital Innovation Hubs to speed safe pilots and scale validated tools (six Czech EDIHs were already listed by early 2023), and it formalises stakeholder governance via the Ministry of Industry and Trade and a standing AI Committee that coordinates implementation and the Action Plan.

The state has signalled meaningful funding and programmes (with an estimated annual budgetary envelope cited by OECD and complementary calls such as TWIST and OP TAK), while a 2023 public consultation that drew 517 responses shows broad public buy‑in - so Czech hospitals planning AI projects gain clearer routes to funding, testing and compliance with the EU AI Act.

For more detail see the Cedefop NAIS summary, the OECD AI policy dashboard, and the Global Legal Insights analysis of the legal and funding context.

“The advent of artificial intelligence represents a significant opportunity for the transformation and modernisation of Czech industry. That is why we at the Ministry have decided to assume the leading role in implementing AI into the Czech legal system and to actively support its development and practical application.”

For Czech healthcare providers and vendors the regulatory landscape is now a fast-moving practical reality: the EU AI Act (entered into force in 2024) layers risk‑based AI obligations on top of existing rules so that virtually every AI medical device, diagnostic algorithm or triage app will be treated as high‑risk and must meet new requirements for risk management, data governance, human oversight, transparency and post‑market monitoring; see the sector briefing on health impacts for practical high‑risk examples (Access Partnership: EU AI Act - health sector briefing on health impacts).

Implementation in Czechia follows the EU timetable and requires national designation of competent authorities (the Ministry of Industry and Trade, the Office for Personal Data Protection and other bodies have been named as likely candidates), integrated conformity assessments with MDR/IVDR for AI‑enabled devices, and steep penalties that “put AI compliance on the same strategic level as GDPR” (fines up to €35m or 7% of turnover - see the practical legal overview for Czech implications at DLA Piper and the operational checklist from Gesund.ai).

Practical takeaways for Czech hospitals: map devices against Annex I/III, embed human‑in‑the‑loop controls and data‑lineage records now, and plan Notified Body engagement early to avoid procurement delays as the phased EU deadlines approach (DLA Piper: Navigating the EU AI Act - Czech legal implications).

“If we see that the standards and guidelines are not ready in time, we should not rule out postponing some parts of the AI Act.”

Data governance in Czech healthcare sits at the intersection of EU law and national practice: health, genetic and biometric records are “special categories” under GDPR and may only be processed under narrow conditions (GDPR Article 9 on processing of special categories of personal data), while the Czech Data Processing Act (Act No.

110/2019 Coll.) and the Office for Personal Data Protection (ÚOOÚ) set local rules and enforcement expectations - so any AI pilot that uses patient records must treat data minimisation, purpose limitation and strong technical safeguards as core requirements.

Practical triggers to watch: appointment of a Data Protection Officer when core activities include large‑scale processing of health data, mandatory DPIAs for high‑risk uses (for example large datasets or automated clinical profiling), 72‑hour breach notification duties, and tight limits on cross‑border transfers.

Anonymisation is not a panacea in a country the size of the Czech Republic - the literature warns that rare‑disease or richly linked clinical datasets are often only pseudonymised and remain re‑identification risks - so pair pseudonymisation and encryption with strict access controls and contractual processor clauses.

For clinical research, follow the regulator's filing rules and separate data‑processing consents as advised by SÚKL to avoid regulatory pitfalls; for a clear legal baseline see the GDPR Article 9 summary and SÚKL guidance on GDPR and clinical trials.

“It is not permissible to mix a consent with the conduct of a clinical study in patients or healthy volunteers and a consent with personal data processing, as this concerns two separate consents. A trial subject has the right for information from the data controller pursuant to Articles 13 to 15 of part 2 of the General Data Protection Regulation (GDPR) even where the processing is based upon a legitimate reason.”

Liability, clinical responsibility and procurement in Czech hospitals must be treated as clinical risk management: the EU's evolving product‑liability rules and the AI Act mean hospitals are no longer passive buyers but active safety gatekeepers, with operator duties, DPIA triggers and human‑in‑the‑loop requirements that can create joint liability if governance lapses.

Procure with dual‑compliance in mind (MDR/IVDR plus high‑risk AI obligations), insist on full technical documentation, automatic operational logs and post‑market monitoring, and embed clear SLA/change‑management rules for software updates so responsibility for model drift or insecure integrations is contractually allocated - practical checklists and obligations are usefully summarised in ARROWS' compliance guide for healthcare AI. Contracts should include warranties on conformity and data processing, indemnities, audit rights and explicit update/versioning responsibilities; courts can compel disclosure of evidence and, where producers fail to cooperate, procedural presumptions may shift the burden of proof in favour of claimants (a major point in recent EU liability analyses).

For procurement teams the simple checklist is: map each system to regulatory categories, require conformity evidence before purchase, document clinician training and oversight procedures, and make supplier audits a standing condition of contract - these steps turn legal risk into a measurable procurement control rather than an unpredictable exposure (see ARROWS' legal checklist and Two Birds' guide to EU product‑liability reforms).

Adoption in Czech healthcare today looks like a cautious, evidence‑driven ripple rather than a flood: Czech laboratory medicine is part of the European conversation (see the multi‑centre survey that includes the Medical Faculty in Pilsen, Charles University) showing labs and clinics are actively assessing their digital readiness and current AI use (European laboratory AI adoption survey on PubMed), while market research highlights common bottlenecks hospitals must face - only about 30% of pilots reach production and data, integration and skills gaps are the usual culprits, even as 60% of execs report AI budgets outpacing IT spend (Healthcare AI Adoption Index - adoption metrics and co‑development trends).

Real European use cases give Czech providers a roadmap: AI imaging overlays and prioritisation tools have halved report turnaround in some sites (critical CTs and hemorrhage cases can be surfaced to clinicians in seconds), proving the

capacity‑multiplier

thesis that lets teams handle more scans without new hires (AI imaging use cases in Europe - imaging and workflow case studies).

The practical takeaway for Czech hospitals is clear - start where data are clean (labs, imaging, admin), measure ROI within the first year, and favour co‑development with trusted partners so pilots escape the POC trap and become lasting workflow upgrades.

The Czech AI health‑tech scene in 2025 feels energised and pragmatic: homegrown winners like Kardi Ai have turned a comfortable, wearable chest‑strap into a medically certified monitoring service - MDR Class IIa cleared, prescribed by hundreds of clinicians and credited with catching 220+ cardiac events while serving over a thousand users - after a €1.5M seed round and follow‑on funding to scale across Central Europe (Kardi Ai's at‑home heart monitor).

At the same time radiology‑focused teams such as Carebot are moving from research to clinical deployment with European certification and early investment rounds that validate image‑analysis tools for hospitals.

Those success stories sit inside a broader national push - TWIST and OP TAK funding calls, a network of technology incubators (178 projects supported, ~27% AI‑focused) and public programmes that have channelled substantial CZK funding into Deep Tech and health AI - helping translate pilots into paid pilots and pilots into products (Global Legal Insights: Czech AI market & funding).

The combined effect: more co‑development deals with insurers and clinicians, clearer certification pathways, and startups building products patients can wear and insurers will cover - concrete momentum that hospital procurement teams can tap without waiting for a distant “AI future.”

“Anyone with a smartphone can use our service, and it costs about as much as a family subscription to Netflix per month for full real-time monitoring.” - Stephen Burke, co‑founder, Kardi Ai

Begin simply and legally: treat any AI pilot in Czech hospitals as a regulated clinical project and follow a short, practical checklist so pilots don't become compliance headaches - first, classify the system (AI Act high‑risk? also a medical device under MDR/IVDR) and run a compliance gap analysis as recommended by ARROWS (ARROWS legal and regulatory guidance on AI in healthcare); second, screen and complete a DPIA early (large‑scale health data or automated clinical profiling will almost always trigger one) using an established template such as the d.pia.lab/IAPP resources (DPIA template from IAPP / d.pia.lab); third, lock down data governance - pseudonymisation, strong encryption, clear data‑curation agreements and DPO oversight - because poor input data creates joint liability; fourth, bake in human oversight, logging and post‑market monitoring (automatic logs retained per AIA obligations) and require supplier warranties, audit rights and indemnities in contracts; fifth, start where evidence is strongest (labs, imaging, admin) and pair each pilot with clinician training and a measurable ROI metric so trials scale into production; finally, build skills across the team - non‑technical staff can gain workplace AI competence in a focused Nucamp course like AI Essentials for Work (Nucamp AI Essentials for Work syllabus and registration) - because practical compliance plus simple, repeatable steps turns legal risk into a controllable part of safer, faster innovation.