Top 10 AI Prompts and Use Cases and in the Healthcare Industry in Netherlands

AI is reshaping Dutch healthcare because it promises faster, more accurate care while raising unique legal and privacy stakes: medical AI often counts as

high‑risk

under the EU AI Act and must meet GDPR and medical device standards (MDR/IVDR), so hospitals and startups alike need clear governance, DPIAs and human oversight.

The Dutch Data Protection Authority (AP) is already coordinating AI oversight and the government's human‑centred vision stresses public values - the national algorithm register now lists hundreds of live systems - so responsible rollout matters as much as the technology itself.

Practical skills matter on the ground: for clinicians and managers learning to write safe prompts and run compliant pilots, Nucamp's 15‑week AI Essentials for Work bootcamp teaches prompt writing and workplace AI use.

Read official guidance on the EU AI Act from the AP and a legal overview of Dutch AI rules for more context.

Bootcamp	Length	Early bird cost	Links
AI Essentials for Work	15 Weeks	$3,582	AI Essentials for Work syllabus | Register for AI Essentials for Work

Selection of the top 10 prompts and use cases prioritised what matters most for Dutch healthcare: real clinical benefit, clear regulatory footing and practical deployability in hospitals and clinics.

That meant favouring use cases the European Commission flags as high‑value (early detection, resource optimisation and AI‑powered diagnostics) and those that can be tested with trusted health data under the emerging EHDS, while avoiding high‑risk practices that invite GDPR enforcement - a hard lesson from the Haga Hospital case where the Autoriteit Persoonsgegevens imposed a €460,000 fine after 197 staff improperly accessed records.

Each candidate prompt was scored against three evidence-backed filters: clinical impact and workflow fit (can it speed diagnosis or free clinician time?), data and privacy readiness (is training/evaluation data available under EHDS/GDPR rules?), and regulatory/responsibility risk (does it align with the EU AI Act and product liability expectations?).

Practical readiness and organisational strategy also mattered: providers without an AI strategy face compliance gaps, so prompts that supported pilots, DPIAs and clear human oversight were favoured.

Links used to vet choices include the AP enforcement record and the European Commission's AI in healthcare guidance to ensure every recommended prompt balances innovation with real-world Dutch legal and operational constraints.

Selection Criterion	Why it matters (source)
Regulatory & privacy readiness	AP GDPR enforcement: Haga Hospital GDPR fine for improper staff access
Clinical impact & workflow fit	European Commission guidance on artificial intelligence in healthcare
Organisational strategy & deployment	Pinsent Masons Out-Law analysis: healthcare providers' AI strategy and EU AI Act requirements

In Dutch hospitals and clinics, GPT‑NL–powered clinical decision support promises to act as a practical, explainable “second opinion” that surfaces likely diagnoses, relevant guidelines and prescription checks right in the workflow - but success depends on trust, not just accuracy.

A recent JMIR systematic review outlines eight trust factors that matter for adoption - from system transparency and clinician training to usability, clinical reliability, external validation, ethical safeguards, human‑centred design and the ability to customise while preserving clinician control (JMIR systematic review on trust factors for AI clinical decision support (AI‑CDSS)).

For Dutch teams this means pairing technical work - prompt tuning for Dutch language models and validated imaging aids - with governance steps like DPIAs and GDPR‑aligned pilots; practical how‑to's for those compliance steps are collected in Nucamp's guide on running GDPR and DPIAs for healthcare AI (Nucamp guide to GDPR and DPIAs for healthcare AI (AI Essentials for Work syllabus)).

The takeaway is concrete: implement GPT‑NL prompts that prioritise explainability and clinician control to turn promise into reliable, day‑to‑day support clinicians trust.

Key trust themes (JMIR)	Why it matters
System Transparency	Explainability builds clinician confidence
Training & Familiarity	Education reduces misuse and resistance
System Usability	Workflow integration prevents disruption
Clinical Reliability	Consistent accuracy underpins trust
Credibility & Validation	External testing across contexts is essential
Ethical Consideration	Liability, fairness and patient rights must be addressed
Human‑Centred Design	Patient and clinician needs guide adoption
Customization & Control	Tailoring preserves clinician autonomy

Customer service chatbots - the Wellframe and SkinVision‑style patient assistants - are practical first AI pilots for Dutch care settings because they tackle the everyday friction that clogs clinics: 24/7 appointment booking, symptom assessment and triage, medication reminders and simple insurance or billing queries, all while routing complex cases to a human clinician and preserving audit trails for GDPR‑aligned governance.

These conversational assistants scale patient access without adding front‑desk headcount, reduce wait‑time friction that leaves people stuck on hold, and collect structured pre‑visit data that makes consultations faster and safer - exactly the outcomes many Dutch providers want to test under EHDS‑compatible pilots.

For concrete use cases and triage flows, see the practical list of chatbot use cases and benefits in Chatbase's roundup, and for a realistic procurement playbook (build vs buy, integrations, and safety gates) consult TATEEDA's decision guide on healthcare AI chatbots to choose the right path for your organisation.

Use case	Typical benefit
Appointment scheduling & reminders	Fewer no‑shows, lower call volumes
Symptom checking & triage	Faster routing to appropriate care
Medication management	Improved adherence via reminders
Patient feedback & follow‑up	Higher satisfaction and actionable insights

“When platform templates cap autonomy and integrations run out of road, that is your signal to develop a custom healthcare AI chatbot: you need reliable tool‑use policies, deterministic fallbacks, and a model that does not ‘forget' safety when prompts get messy.”

AI agents that automate the front desk are already moving from pilot to practice: Sully.ai bundles a Check‑in Agent, Receptionist Agent and Scribe Agent to turn intake, scheduling and real‑time charting into a single, EMR‑integrated flow - the result is less waiting, fewer clicks and clinicians who actually finish notes before they leave clinic.

Parikh Health's case study shows the payoff in plain terms (a personalised AI check‑in, automated form updates and EMR writes) with a 10x drop in operations per patient, a 90% fall in reported burnout and a threefold efficiency boost (Parikh Health Sully.ai case study on AI check-in and EMR integration).

For teams weighing buy vs build, Sully's modular agents and EHR plugins make it easy to start small and scale; read the broader agent playbook and outcomes in Sully's customer stories and product writeups (Sully.ai customer stories and product writeups about modular agents and EHR plugins).

The vivid payoff is simple: clinicians reclaim evening hours and eye contact - no more after‑hours charting - while clinics see smoother throughput and cleaner records.

Site / Metric	Impact
Parikh Health	10x decrease in operations per patient • 90% decrease in burnout • 3x efficiency
CityHealth	50% decrease in operations per patient • 80% decrease in burnout • ~2.8 hours saved per clinician/day
Sully.ai (platform)	300+ organisations; 2.4M+ medical tasks completed; real‑time EMR integration

“Sully.ai is an all-in-one solution, from patient intake to in-visit interactions with patients, as well as aftercare and follow-up. For us physicians, it's a game-changer.”

For Netherlands pharmacies exploring AI-driven medication safety, practical wins come from pairing image‑and‑weight audit hardware with broader automation and governance: systems like Daifuku/Contec's audit‑i show how combining camera‑based recognition and a built‑in scale can identify drugs and quantities in under a second, generate audit records and centralise learning across sites, while automated prescription management platforms reduce human error and streamline e‑prescribing and barcode checks (Daifuku Audit‑i prescription auditing system, automated prescription management HIPAA e-commerce overview).

In the Dutch context that means running GDPR‑aligned DPIAs, preserving tamper‑proof audit trails for regulators, and following ADC and workflow best practices so robotics and AI free pharmacists for patient counselling rather than replace critical checks; the most concrete benefit is simple and visible - a near‑instant visual verification that turns a pharmacist's half‑second doubt into a documented, reversible action, improving safety and freeing time for care.

“Even though pharmacies and logistics hubs are different in terms of industry and scale, there are many workflow similarities, such as picking, inspection, and distribution. We felt that Daifuku's expertise could help reduce the risk of prescription errors.”

Real‑time triage and prioritisation can be a game‑changer for Dutch hospitals facing high imaging volumes: Enlitic's platform turns messy DICOM studies into AI-ready data with ENDEX/Ensight, standardising series descriptions and creating clinically relevant hanging protocols so incoming cases can be scanned for multiple findings, scored for urgency and routed to the right specialist - helping clinicians see the truly critical cases first rather than wading through inconsistent metadata (Enlitic AI-Ready Data blog post, Enlitic ENDEX data standardization page).

For Netherlands teams this matters not as abstract efficiency but in practical pilots: standardisation reduces manual reordering in PACS, enables reliable priority flags in the radiologist worklist, and creates anonymised, high‑quality datasets that support safe audits and validation under local governance; for a quick local framing of benefits, see Nucamp AI Essentials for Work bootcamp: AI-assisted imaging diagnostics in the Netherlands.

The bottom line: invest in clean, standardised pipelines first, and real‑time triage becomes a dependable workflow tool rather than another source of false alarms.

Medical imaging in the Netherlands is ready for pragmatic AI upgrades that cut waiting times and catch disease earlier: a recent European Radiology Experimental review on AI for image quality and patient safety shows AI methods can optimise radiation doses, reduce scan times and enhance image quality in CT and MRI, improving patient safety and diagnostic confidence.

Industry tools already translate those gains into clinic-ready wins - GE HealthCare AIR Recon DL and Sonic DL MRI solutions promise up to 60% sharper images and as much as an 86% reduction in scan time.

“high‑quality five‑minute knee exam”

Complementary research shows newer generative models can harmonise scans from different vendors and remove motion artifacts, which matters for Dutch hospitals running multi‑site audits or multicentre trials - see the UNC Health report on MRI enhancement models (BME‑X and skull‑strip) - so practical pilots should prioritise harmonisation, explainability and GDPR‑aligned validation to turn faster, cleaner images into earlier, safer diagnoses.

Capability	Reported benefit	Source
MRI scan acceleration	Up to 86% reduction in scan time	GE HealthCare AIR Recon DL MRI solutions
Image quality / SNR	Up to 60% sharper images	GE HealthCare AIR Recon DL MRI solutions
Cross‑scanner harmonisation	Improved reliability and reduced artifacts	UNC Health MRI enhancement models (BME‑X and skull‑strip)

Dutch hospitals and molecular labs looking to bring precision care closer to patients can lean on SOPHiA GENETICS' cloud-native SOPHiA DDM™ platform to make personalized medications practical: the IVDR‑certified system ingests raw FASTQ uploads, harmonises multimodal data and delivers clinician‑ready variant calls and reports that speed decision‑making for oncology and rare disorders while keeping data control local and GDPR‑aligned (SOPHiA DDM platform for genomics).

Recent partnerships and validated clinical‑trial assays show the platform's role in matching patients to targeted therapies and accelerating site selection and enrollment - useful for Dutch centres aiming to run decentralised CGP or participate in biomarker‑driven trials (Precision for Medicine partnership with SOPHiA GENETICS).

For hospitals juggling vendor heterogeneity across sites, SOPHiA's emphasis on scalable, sequencer‑agnostic workflows and community‑shared intelligence promises a tangible payoff: faster turnarounds, fewer repeat tests and a single, auditable trail from sample to treatment recommendation that makes precision medicine operational, not just aspirational.

Capability / Metric	Detail
Regulatory status	IVDR‑certified / CE marked
Platform reach	800+ institutions • 70+ countries
Data scale	2M+ genomic profiles analysed

“SOPHiA UNITY will usher in the next era of precision medicine innovation by bringing together best‑in‑class institutions that are committed to the advancement of oncology research and the use of data‑driven medicine.”

Drug discovery and gene analysis are moving from promise to practice in the Netherlands as AI helps teams sift vast omics and clinical datasets to prioritise targets that are more likely to succeed in humans: Insilico's review highlights that an increasing number of AI‑identified targets are now advancing into experimental validation and clinical trials (AI-powered therapeutic target discovery - Trends in Pharmacological Sciences (Insilico, 2023)), while a 2025 open‑access review frames AI as a transformative force across early drug development steps from target ID to trial design (Integrating AI in drug discovery and early development - Biomarker Research (2025)).

Practical Dutch use cases pair these predictions with local wet‑lab validation - organoids, PDX and high‑content assays - to reduce late‑stage failures, and AI partners report dramatic speedups (one workflow example trims target‑to‑indication matching from months to weeks, making that stalled candidate suddenly visible and triageable).

For hospitals, molecular labs and startups in the Netherlands the takeaway is concrete: invest in high‑quality, multi‑omic datasets and wet‑lab pipelines so AI can do its job - predict, prioritise and guide smarter experiments - while national collaborations and bioinformatics partnerships turn algorithmic leads into validated local assets (Owkin case study: AI-driven target discovery workflows).

Source	Key point
AI-powered therapeutic target discovery - Trends in Pharmacological Sciences (Insilico, 2023)	AI‑identified targets are increasingly validated and entering trials
Integrating AI in drug discovery and early development - Biomarker Research (2025)	AI transforms target discovery through integrated multimodal data
Owkin case study: AI-driven target discovery workflows	Example workflows can match targets to indications in weeks, not months

“In 100 years, we'll look back and say, ‘I can't believe we actually used to test drugs on humans!'”

Surgical and assistive robotics are already earning a place in Dutch acute care by turning physically gruelling tasks into reliable, auditable processes - Stryker's LUCAS 3 chest‑compression system shows how: configurable, battery‑powered compressions at a guideline‑consistent depth (5.3 cm) and rate (≈102/min) deliver up to +60% increased blood flow to the brain versus manual CPR, chest compression fractions up to 93%, and median transition interruptions of only ~7 seconds, which makes prolonged transport or cath‑lab PCI during CPR feasible while reducing caregiver fatigue and x‑ray exposure (Stryker LUCAS 3 EU product page - chest-compression system details).

For Dutch hospitals thinking beyond pilots, the practical questions are operational and regulatory - training, device connectivity for post‑event review, and GDPR‑aligned implementation - and Nucamp's practical guide on running GDPR and DPIAs for healthcare AI helps frame those steps for local deployments (Nucamp AI Essentials for Work - GDPR and DPIA guidance for healthcare AI deployments).

The takeaway is tangible: a compact, connected robot that preserves high‑quality CPR so teams can focus on diagnosis and advanced therapies instead of exhausting manual compressions.

Metric	Value	Source
Compression depth	5.3 cm	Stryker LUCAS 3 technical specifications (US page)
Compression rate	~102 per minute	Stryker LUCAS 3 technical specifications (US page)
Median interruption (manual → mechanical)	~7 seconds	Stryker LUCAS 3 EU product page - transition interruption data
Chest compression fraction	Up to 93%	Stryker LUCAS 3 technical specifications (US page)
Market reach	50,000+ devices globally	Stryker LUCAS 3 overview and global distribution

“If someone had told me about an 8-hour cardiac arrest. I wouldn't have believed it. But this truly happened.”

Claims processing and fraud‑detection pilots in Dutch healthcare promise real savings and faster reimbursements, but they only succeed when privacy and compliance are baked in: the Autoriteit Persoonsgegevens has made clear that lawful training data, robust DPIAs and easy channels for data‑subject rights are non‑negotiable - see the Dutch DPA's recent consultation on “GDPR preconditions for generative AI” for concrete expectations on data curation and purpose limitation (Dutch DPA consultation on GDPR preconditions for generative AI).

Lessons from enforcement are stark: a major fine after the Haga Hospital snooping case (€460,000 and wide‑ranging penalties) underlines why two‑factor authentication, strict logging and role‑based access can't be afterthoughts (Haga Hospital GDPR fine case and enforcement findings).

Operational basics matter too - appointing and publishing a DPO contact, keeping Article 30 processing records and running targeted DPIAs were the very issues the Dutch regulator probed in 2018, so vendors and payers must align claims‑automation and fraud models with local rules before scaling (Netherlands investigation into DPO designation and Article 30 processing records).

The payoff is tangible: compliant pipelines turn opaque model flags into auditable, defensible decisions that protect patients and reduce costly fines.

To turn the promise of AI into everyday wins in the Netherlands, start with small, measurable prompts that fit existing workflows, legal guardrails and local data capacity: use examples already working in Dutch practice - Vitestro's autonomous blood‑draw device and Leiden UMC's ER‑admission model - as blueprints for narrow pilots, and lean on national initiatives like Health RI to ease data access and harmonisation (How Dutch Healthcare Is Implementing AI).

Prioritise use cases with clear benefit (no‑show prediction with proactive outreach is a known win), embed GDPR/MDR checks into design from day one, and partner with in‑house AI teams or trusted vendors so clinical staff retain control.

Primary care readiness is rising too - see research on GP AI readiness - so pilots can safely expand from hospitals into community settings (AI Readiness of Dutch GPs).

For teams building practical prompts and guardrails, structured skills matter: the 15‑week AI Essentials for Work course covers prompt design, GDPR/DPIA workflows and workplace application to turn pilots into compliant, scalable services (AI Essentials for Work syllabus), helping ensure the first prompts improve access and free clinicians for the care patients actually need.

Program	Length	Early bird cost	Link
AI Essentials for Work	15 Weeks	$3,582	AI Essentials for Work syllabus and details