Top 5 Jobs in Healthcare That Are Most at Risk from AI in Belgium - And How to Adapt

Belgian healthcare workers should pay attention to AI now: EY's research shows 70.9% of Belgians have used AI but three out of four fear it will lead to fewer jobs, and the health sector is notably more cautious with lower reported benefits and only about 12% saying AI already affects daily work; that combination - high anxiety, low training and a management–employee perception gap - means roles like transcription, coding and scheduling are especially exposed unless staff upskill quickly.

Practical, hands-on learning is what Belgians want (live workshops 43%, online courses 38%), so pairing the EY insights with targeted programs such as Nucamp's AI Essentials for Work bootcamp - Nucamp registration or digging into EY's findings on Belgian attitudes to AI (EY Belgium employee attitudes to AI report) can help clinicians turn disruption into safer, faster care and new on-the-job skills.

"Consumer concerns about AI responsibility impact brand trust, placing CEOs at the forefront of these discussions. Executives must address these issues by developing responsible strategies to mitigate AI risks and being transparent about their organization's use and protection of AI." - Raj Sharma, EY Global Managing Partner - Growth and Innovation

To pick the Top 5 jobs most at risk in Belgian healthcare the team applied a simple, evidence-led filter rooted in local data: (1) task repetitiveness and rule-based workflows (think heavy note-taking, coding and scheduling), (2) sector readiness and current AI penetration in Belgium, (3) the employee–management perception gap about productivity gains, and (4) measurable adoption signals from health-specific surveys.

That meant prioritising roles where automation already promises quick wins - administrative tasks that GenAI and process automation can handle (EY finds AI could automate up to 45% of administrative work in healthcare) and back-office functions with high error/cost exposure - while cross-checking national stats: Belgium shows strong interest in AI (70.9% have used AI) but only 12% report it affects daily work and the country reports above-average positive financial effects in the EY European AI Barometer 2025, so jobs with routine outputs plus weak upskilling offers are especially exposed.

Health-sector evidence from KLAS's Healthcare AI 2025 survey (256 respondents) and EY's Belgium analysis on employee fears and training gaps steered the weighting: high technical feasibility + low current AI literacy + clear productivity or cost impact = higher risk ranking, which is why transcription, coding and scheduling roles appear near the top of this list.

For full methodology details and the national context, see EY's Belgium analysis and the broader EY European AI Barometer 2025 and the KLAS Healthcare AI 2025 report.

“Those who do not engage with the topic of AI will fall behind. This applies to individual employees as well as to organizations as a whole.” - Adrian Ott, Partner, Forensic leader and Chief Artificial Intelligence Officer | EY Switzerland

Medical transcriptionists and clinical documentation specialists in Belgium face immediate pressure because the core task - turning spoken encounters into structured, billable records - is exactly what generative AI now excels at: Belgian experts flag that AI will reshape patient-record standardisation and even cross‑border translation (Belgian EBCP AI policy brief on patient-record standardisation), while industry deployments show real-world speed and revenue gains when transcription happens during the visit.

Practical pilots and vendors report clinicians saving minutes per appointment (and in some cases reclaiming hours each day), cleaner coding and fewer denials, plus multilingual support that matters in Belgium's Dutch‑/French‑speaking clinics (Commure case study: AI medical transcription clinical and financial impact).

That does not mean instant obsolescence: analyses warn AI drafts are best paired with human review to catch nuance, clinical context and avoid errors, so the safest path for Belgian transcription teams is to own the workflow - learn to validate AI outputs, specialise in complex specialties, and deploy language-aware prompts and templates (see local multilingual assistant pilots for primary care Dutch/French virtual assistant pilots for Belgian primary care).

Picture a clinic where notes are ready five minutes after the visit instead of late into the evening - that's the “so what” that turns risk into a chance to shift from typing to quality assurance and higher‑value clinical work.

“Patients are usually on board and acceptance rates are 95% or higher.”

Medical coders and billing clerks in Belgium are particularly exposed because their day‑to‑day is largely rule‑based, high‑volume work - exactly what Robotic Process Automation (RPA) and Intelligent Document Processing are built to swallow: extracting invoice and claim data, filling forms, reconciling lines and moving entries between siloed systems (see delaware's overview of RPA for Belgian back offices).

That upside comes with real risks: Deloitte's AI and RPA risk services list the full spectrum - technology, people, operational, continuity, cyber, regulatory and financial risks - so a rushed automation program can save hours but amplify errors or compliance gaps if governance is weak.

The practical adapt strategy for Belgian healthcare employers and coders is clear and local: prioritise process selection and readiness assessments, build an RPA Centre of Excellence, train coders in validation, low‑code tooling and IDP, and own exception workflows so bots handle the bulk while humans manage nuance.

so what?

- instead of late-night data entry, coders become revenue protectors who audit and resolve the few complex denials bots surface (and keep hospitals out of costly compliance trouble) - a transition Deloitte recommends as part of any automation roadmap.

Radiologists in Belgium face a unique squeeze: AI tools for imaging are increasingly classed as high‑risk under the EU framework, so deployments trigger not just technical evaluation but parallel obligations under the AI Act and MDR/IVDR - meaning stronger demands for representative training data, bias mitigation, traceability and human oversight (European Society of Radiology recommendations for the European AI Act on imaging AI European Society of Radiology recommendations for the European AI Act).

New EU guidance also explains how conformity assessment, post‑market monitoring and clinical evidence must converge for AI‑enabled devices, and flags practical routes for research and real‑world testing under Article 60 that can power local pilots (EU guidance on convergence of technical and clinical standards for AI-enabled medical devices (Osborne Clarke)).

At the Belgian level, manufacturers and hospitals already work within the MDR framework outlined by FAMHP, so radiology teams should push to lead device evaluation, insist on documented data governance and clinical validation, and treat AI outputs as decision‑support rather than push‑button replacements (Belgian FAMHP guidance on MDR clinical evaluation for medical devices).

The “so what” is sharp: without that governance a single overlooked pixel - or an unrepresentative dataset - can turn a promising pilot into an avoidable safety and compliance headache, but with radiologists steering validation, AI can amplify throughput while protecting patient safety and regulatory compliance.

Pathologists in Belgium are squarely in the sights of digital‑pathology AI: labs are overloaded with slides, biomarkers and increasingly complex quantitation tasks, and that combination - rising demand plus too few specialists - creates a real bottleneck that AI tools can either ease or worsen depending on how they're used.

Clinically validated platforms like Indica Labs' HALO (which powers CE‑IVD and FDA‑cleared workflows and cloud deployments) and evidence‑driven diagnostics from companies such as Owkin show how automated IHC quantification, biomarker screening and standardized image analysis can speed triage, reduce inter‑observer variability and free pathologists to focus on complex interpretation; Belgian experts are already part of these validation efforts (see Owkin's oncology diagnostics work).

The adaptation path is pragmatic: insist on clinically validated, interoperable systems, lead local validation and cloud migration pilots, codify synoptic outputs for reproducibility, and partner with vendors at conferences and user‑group workshops so AI augments judgement instead of replacing it - because when a trusted slide‑review tool flags the rare, subtle case first, patients get to treatment sooner and pathologists keep control of diagnosis quality.

Indica Labs HALO digital pathology platform and Owkin precision diagnostics for digital pathology are useful starting points for labs planning that move.

“Using HALO AP has made an enormous difference to the way I work. It is much easier to review complex cases and consult with colleagues. Frankly, if you took away my digital pathology system I'd probably retire, the reason being I can do things faster and more accurately than I can with glass.”

Hospital administrative staff - schedulers and triage assistants - are squarely in the automation crosshairs because so much of their day is repeatable: booking, reminders, basic triage and billing queries can now be handled by conversational agents and virtual assistants, which the Ciklum guide shows can automate appointment scheduling and routine patient questions; intelligent virtual agents also offer 24/7 access and smoother intake pathways that lift load from front desks (see Pepper's IVA overview).

That doesn't mean frontline roles disappear - successful adaptation in Belgium will be about shifting from data-entry to oversight: train staff to configure and audit bots, own escalation and exception workflows, validate patient-facing prompts and safeguard privacy, and use freed hours for higher-value tasks such as patient follow-up and complex triage; real-world providers already report large time savings (many practices gain 50+ hours a month and answer far more calls after adding a virtual assistant).

Hospitals that pair clear governance with staff reskilling turn a staffing risk into a productivity win and better patient service.

Belgian healthcare workers and employers should treat AI literacy as urgent practice, not paper‑pushing: Article 4 has applied since 2 February 2025 and enforcement by national market surveillance authorities kicks in from August 2026, so start now by inventorying every AI system in use, mapping who (including contractors) interacts with them, and running a risk‑based training programme tailored to roles and clinical contexts; practical guidance is available in the European Commission's AI literacy Q&A (European Commission AI literacy Q&A (Article 4)) and Agoria's implementation guide for Belgian organisations (Agoria AI literacy implementation guide for Belgian organisations).

Keep internal records of training (certificates not required), embed human oversight for high‑risk systems, and combine role‑specific learning with hands‑on skills so clinical teams can validate AI outputs safely - for practical, job‑focused upskilling consider Nucamp's AI Essentials for Work bootcamp (Nucamp AI Essentials for Work bootcamp - register) as a method to turn compliance into capability.

“Being AI literate” is essential alongside traditional literacy skills.