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

Belgium sits at a practical inflection point: policymakers and hospitals are moving from pilot projects to real-world AI in care - from the Belgian EBCP mirror group's policy brief on AI in cancer care to clinical work at UZ Leuven showing algorithms that predict immunotherapy response and glaucoma risk - while vendors such as Aidoc report live deployments in Belgian hospitals that speed neuro triage.

Strong national rules (see the Digital Health Laws and Regulations 2025 overview) and a federal plan to boost a health data & AI strategy mean compliance and data governance matter as much as clinical value; even modest telemonitoring pilots can cut emergency admissions substantially.

Closing the training gap is urgent, so hands-on courses like the AI Essentials for Work syllabus help clinical teams and managers translate strategy into safe, GDPR-aware practice.

Bootcamp	Length	Cost (early bird)	Syllabus / Register
AI Essentials for Work	15 Weeks	$3,582	AI Essentials for Work syllabus · Register for AI Essentials for Work

“AI in Healthcare has arrived. We can't predict how quickly it will evolve, but it's here.” - Romain Seffer, PwC Belgium

Selection of the top 10 prompts and use cases started with a Belgium-first filter: each candidate had to align with the national and regional AI roadmaps and funding streams set out in the Belgian national AI strategy report (for example, regional programmes such as the Flemish action plan, DigitalWallonia4.ai and Innoviris calls), show clear clinical or operational benefit (pilots like remote patient monitoring that can lower emergency admissions by up to 35% were prioritised), and be realistically deployable under existing legal guardrails (MDR/IVDR, GDPR and the new obligations in the Belgium Digital Health Laws and Regulations 2025 overview and the AI Act).

Methodological steps included mapping each use case to: (1) an evidence tier (clinical validation or real-world deployment), (2) a regulatory pathway (device class, AIA risk level, data-sharing model), (3) funding or procurement routes at federal or regional level, and (4) workforce and governance needs (training, explainability and data stewardship).

Cancer-care priorities flagged by the Belgian EBCP mirror group informed oncology prompts in particular. The result: a compact list that is policy-aware, legally practical, clinically meaningful - and ready for a Belgian hospital or health authority to pilot with a named funding or regulatory lever rather than abstract promise.

Predictive cardiovascular risk models - the class of tools often associated with players like HeartFlow and AliveCor - are one of the most immediately practical AI prompts for Belgian care pathways because they can leverage low‑burden wearables and remote monitoring to move prevention upstream of the hospital; a systematic review of wearable machine learning for cardiovascular outcomes shows wrist‑based PPG and multimodal sensors are promising but that most work stalls at a Technology Readiness Level 5, with no clear integrations into health systems yet (JMIR systematic review of wearable ML for cardiovascular outcomes (2022)).

That gap matters in Belgium: existing pilots and remote patient monitoring programmes already demonstrate big operational wins - reducing emergency admissions by up to 35% - but broad clinical adoption will require longer, real‑world validation, robust GDPR‑aware data governance and device verification (heart‑rate measures from some wearables show high concordance with ECG in validation studies) (study showing remote patient monitoring reduces emergency admissions by up to 35%, validation study comparing wearable heart‑rate devices with ECG).

“so what?”

Without that system‑level integration and rigorous validation, a promising wristband remains a nice gadget - not a trusted clinical predictor that can prevent the next admission.

Radiology is where AI moves from promising to practical in Belgian hospitals: vendors like Aidoc offer an enterprise-grade platform (aiOS™) that prioritises findings, connects care teams and plugs into existing IT to streamline CT and MR worklists, while benchmarking studies and market reports flag peers such as Zebra Medical Vision as major players to watch; a comparative study even shows significant disparities between imaging algorithms, so careful selection matters (Aidoc imaging AI comparative study on algorithm performance, IDTechEx AI in medical diagnostics market report).

In practice this can mean AI-augmented ER triage that surfaces critical intracranial haemorrhage or pulmonary embolism earlier and, importantly, a published clinical analysis links adoption of an AI worklist triage system to decreased hospital length of stay for ICH and PE - a concrete operational win that makes the difference between a prolonged admission and faster, safer throughput for patients (Aidoc clinical study on decreased hospital length of stay for ICH and PE).

For Belgian teams, the takeaway is clear: integrate proven triage tools into workflows, benchmark performance locally, and pair deployment with GDPR‑aware governance to turn image AI into measurable patient and system benefits.

Belgium's move toward precision oncology depends on the same rigorous clinical validation that underpins next‑generation sequencing in practice: the PubMed clinical validation study of an NGS‑based Extended RAS Panel (designed to detect 56 RAS alterations) used metastatic colorectal samples from the phase‑3 PRIME trial and even lists Antwerp University Hospital among the collaborators, showing that Belgian centres are part of this evidence base (PubMed clinical validation study of an NGS-based Extended RAS Panel (PRIME trial)).

Translating that level of assay validation into hospital workflows is the practical test for commercial platforms mentioned in this section's title - what matters is verifiable analytic performance, interoperable reporting into oncology pathways, and governance that keeps patient data protected.

For Belgian teams that means pairing validated genomic assays with GDPR‑aware data practices and clear regulatory care pathways; see the Nucamp guide on the practical consequences of the EU AI Act and EHDS implications (Nucamp AI Essentials for Work syllabus - EU AI Act and EHDS guide) and the primer on GDPR‑compliant AI data governance (Nucamp Cybersecurity Fundamentals primer on GDPR-compliant AI data governance) to ensure precision medicine delivers clinical benefit rather than just experimental promise.

Retinal AI screening is moving from proof‑of‑concept to belt‑and‑braces practicality for Belgium: the E‑CLAIR trial, led by UZ Leuven and recruiting 1,200 participants across Flemish centres, is explicitly testing the efficiency and cost‑effectiveness of an AI‑based diabetic retinopathy (DR) pathway tailored to Flanders' fragmented screening landscape (E‑CLAIR diabetic retinopathy AI screening trial (UZ Leuven) - trial details), and complementary evidence shows that a handheld retinal camera with embedded AI can achieve high sensitivity using only one image per eye - opening a practical route to community screening with a pocket‑sized device rather than full clinic exams (single-image handheld retinal camera AI sensitivity study (Int J Retina Vitreous, 2023)).

For Belgian policymakers and hospitals the “so what” is concrete: a validated single‑image workflow could cut grader workload substantially and keep working‑age patients from drifting into preventable sight loss, but only if pilots such as E‑CLAIR pair clinical accuracy with local cost‑effectiveness, clear referral pathways and GDPR‑aware governance before scaling.

Study	Sponsor	Participants	Sites (Flanders)	Status
E‑CLAIR (Diabetic Retinopathy AI screening)	Universitaire Ziekenhuizen Leuven	1,200	ZNA Antwerp; UZA Antwerp; AZ Sint‑Jan Brugge; AZ Turnhout	Active – Recruiting

Digital pathology is now the linchpin for scaling AI‑driven diagnostics in Belgian hospitals: whole‑slide imaging (WSI) turns glass slides into gigapixel files that enable remote consultation, archiving and algorithmic review, and practical guides show how WSI

“revolutionises pathology” by improving access, speed and image quality

(Introduction to whole-slide imaging - Leica Biosystems).

Yet the transition brings real operational headaches familiar to Belgian labs - massive storage needs, variable scanner artefacts and a shrinking pathologist workforce - so automated QC, smart archival strategies and standardized IT are essential to avoid backlogs (Pathology transition from microscopes to whole-slide imaging - Pathology News).

On the compute side, modern pipelines split slides into patches and use GPU‑accelerated toolkits such as MONAI and RAPIDS to extract nuclei, build feature graphs and analyse millions of tiles fast enough to keep up with scanners - a reminder that one slide can be so large it would need a monitor the size of a tennis court at full resolution (Real-time whole-slide image analysis using MONAI and RAPIDS - NVIDIA Developer).

For Belgian teams, the practical takeaway is clear: pair validated WSI workflows with automated QC, sustainable storage and GPU‑aware analytics so AI partners like Paige.AI can augment diagnostic throughput without creating new bottlenecks.

Remote monitoring and consumer wearables are among the most actionable AI prompts for Belgian care pathways - but they arrive inside a dense legal and reimbursement ecosystem that hospitals and vendors must navigate.

Belgium's eHealth Plan and the growing Belgian Integrated Health Record (BIHR) aim to make patient-generated data more interoperable, and NIHDI has already created a telemonitoring framework (notably for heart failure) with some institutional agreements in place, so practical funding routes exist for proven programmes (Belgium digital healthcare trends 2025 – Chambers Practice Guide).

At the same time, the EU Data Act and national guidance give users a right to raw or pre‑processed data from connected devices while excluding “derived” diagnostic conclusions, meaning a smartwatch's heart‑rate or PPG trace must be shareable but the vendor's clinical interpretation does not have to be handed over (EU Data Act implications for medical devices and health devices – CMS Law-Now).

Regulatory classification also matters: many wearables fall outside MDR unless marketed for a medical purpose, and GDPR treats health signals as special‑category data, so legal bases, data processing agreements and Health Data Agency rules must be settled before scaling.

The practical “so what?”: Belgium can turn a wristband into a care‑grade monitoring node - but only if manufacturers, hospitals and payers align on device classification, Data Act access, GDPR safeguards and NIHDI reimbursement pathways to move from gadget to trusted clinical tool (Belgium digital health laws and regulations 2025 – ICLG).

Conversational AI and virtual health assistants offer a real, practical lever for Belgian care teams: they can provide 24/7 symptom triage, multilingual appointment scheduling and automated follow‑ups that keep patients engaged and reduce unnecessary ER visits, as European pilots show (for example, late‑night chats that schedule care or voice follow‑ups after discharge) - a tangible gain for Belgium's stretched after‑hours capacity (conversational AI in European healthcare for patient engagement and after-hours care).

But Belgium's strong data rules change the playbook: the Belgian Data Protection Authority's materials on AI and the GDPR stress transparency, human oversight and DPO involvement, and the BDPA brochure is available in French, Dutch and English to help local teams translate obligations into practice (Belgian Data Protection Authority brochure on AI and the GDPR (French, Dutch, English)).

And because healthcare chatbots can be classed as limited‑ or high‑risk under the EU AI Act, deployments must meet transparency and oversight milestones (for example, the following rule and phased compliance timelines:

you are interacting with an AI assistant

) - plan for clear consent, EHR logging and fast human handoffs to turn a polite, tireless virtual assistant into a safe clinical partner (chatbots compliance and requirements under the European AI Act).

AI for hospital operations and workflow optimization can feel quietly transformative for Belgian hospitals because it tackles a very concrete bottleneck - knowing how many beds, staff and operating rooms will be needed tomorrow or next week.

Belgian research led by Mieke Deschepper and colleagues showed that predictive models produced good short‑term forecasts of required bed capacity during COVID‑19 (BMC Health Services Research: Prediction of hospital bed capacity during the COVID‑19 pandemic (2021)), and a complementary machine‑learning study demonstrated reliable weekly forecasts of inpatient demand (BMC Medical Informatics and Decision Making: Machine learning forecast for inpatient bed demand (2022)).

For Belgian planners, that means AI can convert guesswork into scheduling - preventing a ward from being overwhelmed by a sudden influx and turning expensive overflow into predictable staffing and elective‑case cadence; pairing these models with proven care pathways and reimbursement routes (for example, remote monitoring programmes that reduce emergency admissions) is the next practical step to turn forecasting into operational savings and safer patient flow (Remote monitoring programs reducing emergency admissions in Belgium).

Study	Year	Key finding
Prediction of hospital bed capacity during the COVID‑19 pandemic	2021	Good short‑term predictions of required beds across wards (Belgian authors)
Machine learning based forecast for the prediction of inpatient bed demand	2022	ML models produced reliable weekly inpatient demand forecasts

Genomic AI is finally practical for Belgian labs and drug‑discovery teams because explainable tools collapse the variant‑analysis bottleneck that has long slowed both rare‑disease diagnosis and target discovery: a single exome can reveal 20,000–50,000 variants, and platforms like SeqOne use a 0–100 DiagAI Score to rank candidates so human experts can focus where it matters (SeqOne DiagAI Score for variant prioritization).

Vendors such as Illumina show how AI can be woven into every step of the sequencing pipeline - from DRAGEN‑assisted variant calling to PrimateAI‑3D and PromoterAI for non‑coding effects - making multiomic datasets actionable for both clinical labs and early‑stage drug discovery teams (Illumina AI in genomics platform).

Explainable AI matters in Belgium's regulatory environment: transparency, traceability and IVDR/CE‑IVD compliance let molecular labs scale without losing clinical governance, while explainable tertiary tools (for example Emedgene) promise big time‑savings by surfacing literature‑backed evidence alongside ranked variants (Emedgene explainable AI for variant analysis).

The practical payoff is tangible - a shortlist of fewer than 20 candidates can turn hours of detective work into a focused clinical decision or a credible drug target hypothesis, shortening the path from sequence to meaningful action.

Metric	Value / Source
DiagAI Score	0–100 variant pathogenicity metric (SeqOne DiagAI Score details)
UP² training set	~2.5 million ClinVar variants (SeqOne)
ShortList average	<18 variants with ~96% accuracy (SeqOne)
ACMG prediction accuracy	97% vs ClinVar (SeqOne)

“Emedgene's machine learning simplifies the highly complex task of variant analysis, allowing us to handle more tests every day.” - Dr. Ray Louie, PhD

Mental‑health chatbots are one of the most deployable AI prompts for Belgium's stretched services - offering 24/7, low‑cost CBT‑style support and, in some trials, surprisingly large symptom gains (a clinical trial found a bot called “Therabot” cut depression and anxiety by nearly half), yet Belgium's real‑world experience also flags stark risks that make careful governance essential (Therabot clinical trial evidence and implications for mental health chatbots; Woebot design choices and safety trade-offs analysis).

A mixed‑methods expert analysis in JMIR underscores the professional caution: clinicians saw chatbots as superficial, prone to emotional dependence, and sometimes unable to recognise crisis language - resulting in medium‑to‑low trust scores and explicit guidance that these tools must augment, not replace, human care (JMIR mixed‑methods analysis of clinician perspectives on chatbots).

The “so what?” is tangible for Belgian teams: a chatbot can widen access instantly, but without clinical oversight, transparent data practices and fast handoffs to clinicians it risks doing harm - an urgency made painfully real by widely reported cases tied to harmful chatbot conversations in Europe.

The safe path forward combines evidence‑backed bots with clinician integration, clear consent and GDPR‑aware governance so Belgium keeps the convenience without trading away safety.

“After telling it I was being abused, it wanted to do a grounding exercise with me”

Belgian healthcare teams ready to move from pilots to safe, scalable AI should start by embedding international best practice into local workflows: adopt the FUTURE‑AI principles of fairness, traceability, usability and explainability as a checklist for every procurement and pilot (FUTURE‑AI guideline (BMJ)), mandate local validation and pressure‑testing of models in real clinical settings (echoing EisnerAmper's call for site‑specific evaluation) and set up continuous monitoring so algorithms are audited like other clinical tools rather than

set and forget

systems (EisnerAmper mitigating AI risks in healthcare - local validation framework).

Practical governance steps include clear GDPR‑compliant data agreements, multi‑stakeholder oversight (clinical, IT, legal and patient representatives), and a staged rollout with human‑in‑the‑loop handoffs for high‑risk decisions; one vivid test to adopt immediately is realistic night‑shift pressure testing so models must prove resilience under noisy, incomplete inputs before touching a patient.

Closing the skills gap matters just as much: targeted, workplace‑focused training such as the AI Essentials for Work bootcamp equips managers and clinicians to write prompts, assess vendor claims and translate FUTURE‑AI recommendations into everyday protocols (AI Essentials for Work bootcamp syllabus and registration).

Together, these steps convert promise into predictable, GDPR‑aware clinical value for Belgian hospitals and patients.

Bootcamp	Length	Cost (early bird)	Syllabus / Register
AI Essentials for Work	15 Weeks	$3,582	AI Essentials for Work bootcamp syllabus and registration