Top 10 AI Prompts and Use Cases and in the Healthcare Industry in Czech Republic

Czech healthcare is shifting from pilot projects to practical wins: local networks and a Czech National AI mapping effort already list 200+ companies and spotlight breakthroughs such as DreaMS for molecular discovery, showing that talent and use‑cases are right here at home (see the prg.ai mapping of Czech AI providers).

At the same time global analysis finds 2025 as a year of greater risk tolerance and pragmatic AI adoption - health systems now pick tools that prove ROI, from ambient listening and chart summarization to machine vision and retrieval‑augmented generation - so beginners who learn prompt design and simple RAG workflows can contribute quickly (HealthTech overview of 2025 AI trends in healthcare).

For practical, job‑focused skills that translate to hospital teams and startups, consider the AI Essentials for Work syllabus and training that teaches prompt writing, tool use, and real‑world workflows (AI Essentials for Work syllabus (Nucamp)).

Attribute	Information
Course	AI Essentials for Work
Length	15 Weeks
Cost	$3,582 (early bird) / $3,942 (after)
Syllabus	AI Essentials for Work syllabus (Nucamp)
Registration	Register for AI Essentials for Work (Nucamp)

“It's like finding one character on five pages of solid black text.” - Dr Konrad Wagstyl (on AI detecting subtle lesions)

Selection of the Top 10 combined hard evidence from Czech practice with regulatory common sense: priority went to systems already showing local adoption or clinical pilot data (more than 60% of Czech hospitals use some form of AI, per reporting on national uptake), legal and certification readiness under the EU AI Act and MDR, and clear operational ROI or time‑savings in workflows that matter most to clinicians - minutes literally save brain tissue in stroke care (two million nerve cells can be lost per minute, a striking reminder of why speed matters).

Methodology criteria also required clinician involvement in training/validation, robust data governance (DPIA and audit logs), and architectures that support human‑in‑the‑loop review; see the practical compliance checklist and obligations in the ARROWS legal guide for why transparency, documentation and human oversight shaped prompt design.

Prompt templates were therefore built to match Czech workflows (imaging review, triage checklists, chart summarization, admin automation and RAG for local records), to surface uncertainty and provenance, and to deliver concise, checklist‑style outputs clinicians can verify quickly - practical prompts, not black boxes, that map directly to the use cases listed in this guide.

For context on national adoption and clinical training needs, see the Czech reporting on hospital AI use and the ARROWS compliance roadmap.

Criterion	Why it mattered
Local deployment evidence	Real Czech pilots/usage (>60% hospitals reported)
Regulatory readiness	AI Act / MDR requirements for high‑risk health AI
Clinical impact	Time‑sensitive gains (e.g., stroke minutes → neurons)
Data & governance	DPIA, provenance, audit logs
Human oversight	Templates enforce review, uncertainty and clear next steps

„Na začátku musel být člověk – zkušený radiolog nebo neurolog –, který říkal umělé inteligenci, na co se má dívat…“ - Ondřej Volný

Carebot is a Czech-built image‑reading assistant that has moved quickly from conference demo to hospital testing: a study presented at the European Radiological Congress and reported by Radio Prague International report on Carebot's lung lesion detection study found Carebot detected suspicious lung lesions with 91% accuracy (vs.

29–81% for five radiologists), and the company says its models speed image evaluation by up to 33% while flagging clearly negative chest X‑rays so clinicians can focus on the tricky cases.

Designed to cover X‑ray, CT, MRI and mammography, Carebot has been trained on tens of thousands of anonymised images, is undergoing multi‑site testing in Czech hospitals (including Havířov and Hradec Králové), and is being evaluated in mammography trials at Brno and Šumperk - making it a practical example of how prompt‑aware AI can triage images and generate concise, verifiable reports for busy Czech radiology teams.

See Carebot's company page for product details and trial updates.

Attribute	Information
Reported performance	91% detection (study reported at ECR)
Modalities	X‑ray, CT, MRI, mammography
Training data	Tens of thousands of anonymised images
Speed / workflow benefit	Up to 33% faster image evaluation
Testing sites (Czech)	Havířov, Hradec Králové; mammography studies in Brno and Šumperk

“The best combination out there is a doctor working together with AI.”

Aireen is emerging as a practical, Czech‑rooted screening tool for diabetic retinopathy that blends speed with strong diagnostic performance: a multicentre real‑world study led by researchers at the Military University Hospital Prague and Charles University tested 1,274 patients and found Aireen's fundus‑image analysis reached 92.1% sensitivity and 90.7% specificity - outperforming both general ophthalmologists and retina specialists on the same one‑field, non‑mydriatic photographs - and it flagged inadequate images in about 9.3% of cases so technicians know when to retake the shot (119 of 1,274).

Beyond the numbers, Aireen delivers results in under 30 seconds and is camera‑agnostic, which makes it a practical option for community screening programs and primary‑care settings across the Czech Republic; see the Czech real‑world diabetic retinopathy study on PubMed and the Aireen device overview and certification details.

Attribute	Value
Study cohort	1,274 patients
Excluded (inadequate image)	119 (9.3%)
Prevalence of any DR (DRB)	31.9%
Sensitivity (Aireen)	92.1% (95% CI: 89.3–94.9)
Specificity (Aireen)	90.7% (95% CI: 88.7–92.7)
Result time	<30 seconds

This study underscores the significant potential of the Aireen AI screening system in the detection of DR by comparing the performance of general ophthalmologists, retina specialists, and the Aireen AI system against a clinical reference standard provided by a DRB. The findings suggest that the Aireen AI system can effectively enhance DR screening processes, offering a reliable and efficient alternative to traditional methods. Its integration into clinical practice could lead to improved screening accuracy, potentially benefiting a larger population of patients with diabetes through earlier and more precise detection of DR. This study supports the continued development and implementation of AI technologies in ophthalmology to advance patient care and screening efficiency.

Acute stroke care in the Czech Republic is quietly shifting from hustle to hyper‑focused speed: 23 specialised stroke centres now route CT/CTA and clinical images through AI platforms that flag large vessel occlusions and haemorrhages within minutes, speeding triage and neurointerventional notification so treatment decisions happen while the clock still favors recovery - studies and reports show AI can cut key notification times by tens of minutes (for example, early Viz.ai evidence documented up to 52 minutes saved and the VALIDATE study reported a 39.5‑minute reduction in specialist notification), and local practise proves it works on the ground - University Hospital Ostrava has used Brainomix 360 for five years to streamline imaging review and improve timely interventions for a catchment of roughly 1.6 million people.

National research networks such as STROCZECH (which links 9 comprehensive and 15 primary stroke centres) and European programmes like UMBRELLA are aligning data, protocols and federated models so Czech centres can safely scale AI‑assisted CT/CTA interpretation without losing clinician oversight; see the CzechTrade overview for the national snapshot, the Brainomix Ostrava case study for a local example, and Viz.ai's position on AI becoming a stroke-care standard.

Metric	Detail
Stroke centres using AI	23 specialised centres (national report)
STROCZECH network	9 comprehensive + 15 primary centres (interconnected research network)
Local case study	Brainomix 360 at University Hospital Ostrava (5 years; ~1.6M population served)
Notable time‑savings	Viz.ai evidence: up to 52 min faster (initial study); VALIDATE: 39.5 min faster specialist notification

"The Brainomix 360 software provides immense value and clinical benefits in several ways. Firstly, it speeds up the diagnosis process, allowing for quicker treatment decisions, which is critical in stroke care." - Dr. Ondrej Volny

KardiAI use cases in Czech cardiology often start with portable, AI‑enabled ECGs that put reliable rhythm data into clinicians' hands: devices such as AliveCor's KardiaMobile 6L can record a medical‑grade, six‑lead EKG in about 30 seconds and deliver FDA‑cleared rhythm determinations for AF, bradycardia and tachycardia - making instant, sharable tracings a practical tool for outpatient clinics, telemedicine follow‑ups and remote monitoring programs (KardiaMobile 6L six‑lead ECG product page).

AliveCor's broader platform offerings also support integration with clinician workflows and enterprise monitoring, so rhythm alerts and longitudinal ECG records can feed into hospital review or population health programmes (AliveCor AI‑enabled ECG platform overview).

For Czech hospitals and primary‑care teams learning prompt design and RAG workflows, pairing concise AI ECG summaries with documentation automation helps turn short, actionable readings into verified clinical notes that save time and focus specialist attention where it matters most.

DNAi - Pathology and dermatology image analysis: Czech work in digital pathology is moving from proof‑of‑concept to practical lab tools that actually help pathologists find the smallest, most consequential patterns on a slide - remember that a single slide can contain hundreds of thousands of cells and only a handful might be cancer, a vivid reason to bring machine‑scale eyes to the microscope (see Novartis' account of AI decoding pathology images).

Local research groups are already developing AI methods for cancer diagnosis from surgical samples at Masaryk's Faculty of Informatics (Masaryk University AI digital pathology projects), while commercial platforms like Indica Labs HALO AI train-by-example pathology tools show how segmentation, phenotyping and SlideQC pipelines can be built and adapted without heavy coding - useful for transplanting workflows into Czech hospital labs.

Reviews of AI in histopathology underscore real clinical promise and the need for validation in domain‑specific tasks such as gynaecological slides (Review of AI in gynaecological pathology (PubMed)), so DNAi‑class solutions should pair transparent models, human‑in‑the‑loop checks and clear QC flags to make automated reads trustworthy in routine Czech practice.

Medevio-style administrative automation brings together ambient scribing, smart coding agents and RCM automation so Czech hospitals can turn paperwork into time for patients: by automating note generation, coding checks and first‑pass claims workflows these systems mirror the gains reported by leading vendors - Eleos' documentation tools claim more than a 70% cut in charting time, while AI-native platforms speed revenue‑cycle work and prior‑authorization steps (one case study on an AI RCM workflow cut a multi‑week authorization down to five days).

In practice this means fewer late notes, faster claims and fewer cancelled surgeries or blocked ORs when capacity tools and scheduling assistants are combined with documentation agents (operational AI has been shown to reduce surgery cancellations by up to 40% and boost staff productivity).

For Czech teams starting small, focus on three prompt patterns: (1) ambient scribe templates that capture the clinical checklist, (2) coding‑check prompts that surface missing CPT/ICD elements, and (3) RAG prompts that pull verified chart snippets for appeals - practical steps that free clinicians from paperwork and cut denial cycles.

See Nucamp's guide to automation in Czech practice and Eleos' documentation results for concrete examples of what to expect in implementation (Nucamp AI Essentials guide to documentation automation in Czech healthcare, Eleos AI documentation outcomes and research, Qventus operational AI solutions for hospitals).

Metric	Illustrative result
Documentation time	~70% reduction (Eleos)
Surgery cancellations	Up to 40% reduction (Qventus)
RCM admin lift	50–70% reduction in RCM tasks (athenahealth reporting)
Prior authorization	Case example: from 6–8 weeks to ~5 days (athenahealth case study)

Applifting's track record building and launching digital products, coupled with a Prague presence and local contact points, positions them as a realistic partner for Czech hospitals and startups wanting to prototype surgical support and intraoperative monitoring tools - everything from preoperative AI‑driven segmentation for personalised surgical plans to real‑time video analysis that evaluates incision depth and angle in the OR. Czech surgical teams exploring prompt‑aware workflows can lean on product design and engineering partners to translate the clinical needs described in recent reviews - faster, AI‑assisted planning that changed procedures in four out of ten lung segmentectomies, and intraoperative systems that give immediate feedback - to usable dashboards, haptic integrations and wearable recovery trackers that close the loop from theatre to home.

For teams starting this journey, see Applifting's case studies and contact options for product design support and consult the Czech overview of AI's role in surgery for practical examples and technical possibilities (Applifting case studies and Prague product design contact, Prolekare article: AI in surgery real-time feedback and monitoring).

Datlowe's HAIDi brings a practical, Czech‑ready approach to infection surveillance by tapping directly into existing EHRs to surface infections and the risk factors that cause them - uncovering signals hidden in free‑text clinical notes and producing one‑click reports teams can act on.

Built to be deployed securely inside hospital infrastructure, HAIDi claims it can detect up to 5× more healthcare‑associated infections than traditional methods and eliminate as much as 90% of routine data‑collection work, then adds automated antibiograms and trend monitoring so antibiotic stewardship and targeted prevention become operational instead of aspirational; see the Datlowe HAIDi Czech infection surveillance product page for feature details and local references.

Those performance claims echo broader evidence for real‑time nosocomial surveillance systems improving detection and MDRO reporting accuracy in peer‑reviewed work, which is why Czech infection‑control teams at Nemocnice Jihlava, Nové Město na Moravě and Krajská zdravotní report time savings and clearer, department‑level feedback that drives concrete prevention measures (Datlowe HAIDi Czech infection surveillance product page, RT‑NISS real-time nosocomial infection surveillance study (BMC Infectious Diseases)).

A vivid test: instead of sifting pages of charts for a missed MDR report, HAIDi points infection teams straight to the few records that matter now, freeing clinicians to close the loop on prevention.

Attribute	Value
Detection uplift	Up to 5× more HAIs vs. traditional methods
Data collection reduction	Up to 90% fewer manual tasks
Local footprint	30+ hospitals; 18,000+ beds
Key capabilities	EHR integration, free‑text parsing, one‑click reports, antibiograms

“HAIDI is a great application, that has helped us a lot with surveillance of healthcare-associated infections. We do not have to review health records manually and we can focus on infection prevention in individual departments.” - Mgr. Petra Vavřinová, Leading Infection Control Nurse (Nemocnice Jihlava)

DreaMS, a Czech-born AI from teams at IOCB Prague and CIIRC CTU, is shaping how local labs approach drug discovery, molecular analysis and toxicology screening by turning mountains of mass‑spectra into searchable chemical insight: the model was pretrained on tens of millions of spectra across plants, microbes, food, tissue and soil to build a so‑called DreaMS Atlas - an “internet of spectra” that surfaces hidden chemical relationships and speeds up identification of unknown molecules (the team's work appears in Nature Biotechnology).

In practical Czech settings this means faster prioritisation of novel natural products for pharmacology, smarter forensic toxicology screens and earlier flags for agrochemical links to disease: the model even learned to detect fluorine, an element present in roughly one‑third of drugs and agrochemicals, after focused fine‑tuning.

For hospitals, startups and CROs in the Czech Republic that want to add prompt‑aware workflows, DreaMS offers a concrete example of AI that translates complex spectral data into testable hypotheses and drug‑discovery leads - a leap from spectra to actionable leads that can shorten bench‑to‑clinic cycles.

See the CIIRC and IOCB Prague announcement about DreaMS and the full DreaMS Nature Biotechnology paper for details.

“The DreaMS model was trained on tens of millions of spectra from diverse organisms and environments – plants, microbes, food, tissue, and soil.”

Upheal brings AI‑generated progress notes and session analytics to mental‑health teams who want to spend less time on documentation and more time with patients - its scribe can create SOAP, GIRP, DAP and other insurer‑ready formats, auto‑build “Golden‑Thread” treatment plans, and surface talking ratios, sentiment and cadence in easy timelines so clinicians see patterns across care episodes; see Upheal's overview of secure AI progress notes and practice tools for clinicians in detail at Upheal's site.

The platform supports HIPAA, SOC 2 Type II and GDPR‑minded controls, offers free core features with premium tiers for Golden‑Thread plans and HIPAA‑secure video capture, and captures sessions via built‑in video, Zoom integration or a browser recorder so notes can be generated in minutes - vendors report clinicians saving hours per week after adoption.

Upheal also asks for explicit opt‑in before using de‑identified session data for model training and documents retention policies (de‑identified transcripts retained 1 year; de‑identified datasets up to 5 years), making it a practical starting point for Czech clinics and private practices exploring RAG workflows, automated notes and measurable therapy analytics; explore its session analytics and treatment‑planning features for examples and workflow ideas.

Attribute	Value
Core features	AI progress notes, session analytics, treatment plans, compliance checker
Compliance	HIPAA, SOC 2 Type II, GDPR controls
Capture / integrations	Built‑in video, Zoom, Chrome recorder, Google Calendar
Pricing model	Free core plan; premium tiers for advanced features
Data retention (opt‑in)	De‑identified transcripts: 1 year; de‑identified datasets: 5 years
Note formats	SOAP, DAP, BIRP, GIRP, EMDR, Mental Status Exam, custom templates

“We aim to augment the therapist's role, making their work more efficient and impactful. Rest assured, that our mission is NOT to replace therapists with AI. We firmly believe in the irreplaceable value of human therapists in delivering effective therapy.”

Ready to move from reading about Czech healthtech to doing practical work? Start small, pick one repeatable workflow (documentation, a screening task or an imaging triage prompt), and build prompt templates that force clarity, provenance and a human sign‑off - the IPVZ roadmap for clinician training stresses exactly this: learn how models make decisions and how to prompt them safely (IPVZ interview on clinician roles and safe prompting).

Pair that hands‑on practice with the growing Czech guidance on safe, staged rollouts - and don't forget governance: DPIAs, clinical validation and patient consent.

For a structured learning path that teaches prompt writing, RAG workflows and workplace automation, see the practical course syllabus (AI Essentials for Work syllabus - Nucamp), and keep national evidence and ethical limits in view by reading how Czech medical faculties frame AI's role in care (Czech medical faculty perspective: How AI will change medicine - 1st LF UK).

The right small project plus verified prompts can turn paperwork into time for patients and create immediate, verifiable wins in Czech clinics.

Attribute	Information
Course	AI Essentials for Work
Length	15 Weeks
Cost	$3,582 (early bird) / $3,942 (after)
Syllabus	AI Essentials for Work syllabus (Nucamp)
Registration	Register for AI Essentials for Work (Nucamp)

„Umělá inteligence v radiologii už je a určitě zde zůstane. Dokáže analyzovat obrovské množství snímků rychleji než radiolog.“ - doc. Andrea Burgetová