Evidence map›Paper›PMID 41213096›Full record

ArticleJournal of medical Internet research2025

AI Act Compliance Within the MyHealth@EU Framework: Tutorial.

Monika Simjanoska Misheva, Dragan Shahpaski, Jovana Dobreva, Djansel Bukovec, Blagojche Gjorgjioski, Marjan Nikolov, Dalibor Frtunikj, Petre Lameski, Azir Aliu, Kostadin Mishev and 1 more

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Monika Simjanoska MishevaFaculty of Computer Science and Engineering, Saints Cyril and Methodius University of Skopje, Rugjer Boshkovikj 16, Skopje, 1000, North Macedonia, 0038976472195.ORCID 0000-0002-5028-3841
Dragan ShahpaskiSorsix, Skopje, North Macedonia.ORCID 0009-0000-9106-7726
Jovana DobrevaFaculty of Computer Science and Engineering, Saints Cyril and Methodius University of Skopje, Rugjer Boshkovikj 16, Skopje, 1000, North Macedonia, 0038976472195.ORCID 0000-0001-7382-2062
Djansel BukovecZan Mitrev Clinic, Skopje, North Macedonia.ORCID 0009-0008-1522-0678
Blagojche GjorgjioskiAdagon, Skopje, North Macedonia.ORCID 0009-0008-3976-1241
Marjan NikolovSorsix, Skopje, North Macedonia.ORCID 0009-0005-9739-322X
Dalibor FrtunikjSorsix, Skopje, North Macedonia.ORCID 0009-0004-9288-0726
Petre LameskiFaculty of Computer Science and Engineering, Saints Cyril and Methodius University of Skopje, Rugjer Boshkovikj 16, Skopje, 1000, North Macedonia, 0038976472195.ORCID 0000-0002-5336-1796
Azir AliuMinistry of Health, Skopje, North Macedonia.ORCID 0000-0002-1575-170X
Kostadin MishevFaculty of Computer Science and Engineering, Saints Cyril and Methodius University of Skopje, Rugjer Boshkovikj 16, Skopje, 1000, North Macedonia, 0038976472195.ORCID 0000-0003-3982-3330
Matjaž GamsJožef Stefan Institute, Ljubljana, Slovenia.ORCID 0000-0002-5747-0711

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Unlabelled: The integration of artificial intelligence (AI) into clinical workflows is advancing even before full compliance with the European Union Cross-Border eHealth Network (MyHealth@EU) framework is achieved. While AI-based clinical decision support systems are automatically classified as high risk under the European Union's AI Act, cross-border health data exchange must also satisfy MyHealth@EU interoperability requirements. This creates a dual-compliance challenge: vertical safety and ethics controls mandated by the AI Act and horizontal semantic transport requirements enforced through Open National Contact Point (OpenNCP) gateways, many of which are still maturing toward production readiness. This paper provides a practical, phase-oriented tutorial that enables developers and providers to embed AI Act safeguards before approaching MyHealth@EU interoperability tests. The goal is to show how AI-specific metadata can be included in the Health Level Seven International Clinical Document Architecture and Fast Healthcare Interoperability Resources messages without disrupting standard structures, ensuring both compliance and trustworthiness in AI-assisted clinical decisions. We systematically analyzed Regulation (EU) 2024/1689 (AI Act) and the OpenNCP technical specifications, extracting a harmonized set of overlapping obligations. The AI Act provisions on transparency, provenance, and robustness are mapped directly onto MyHealth@EU workflows, identifying the points where outgoing messages must record AI involvement, log provenance, and trigger validation. To operationalize this mapping, we propose a minimal extension set, covering AI contribution status, rationale, risk classification, and Annex IV documentation links, together with a phase-based compliance checklist that aligns AI Act controls with MyHealth@EU conformance steps. A simulated International Patient Summary transmission demonstrates how Clinical Document Architecture/Fast Healthcare Interoperability Resources extensions can annotate AI involvement, how OpenNCP processes such enriched payloads, and how clinicians in another member state view the result with backward compatibility preserved. We expand on security considerations (eg, Open Worldwide Application Security Project generative AI risks such as prompt injection and adversarial inputs), continuous postmarket risk assessment, monitoring, and alignment with MyHealth@EU's incident aggregation system. Limitations reflect the immaturity of current infrastructures and regulations, with real-world validation pending the rollout of key dependencies. AI-enabled clinical software succeeds only when AI Act safeguards and MyHealth@EU interoperability rules are engineered together from day 0. This tutorial provides developers with a forward-looking blueprint that reduces duplication of effort, streamlines conformance testing, and embeds compliance early. While the concept is still in its early phases of practice, it represents a necessary and worthwhile direction for ensuring that future AI-enabled clinical systems can meet both European Union regulatory requirements from day 1., risks such as prompt injection and adversarial inputs), continuous postmarket risk assessment, monitoring, and alignment with MyHealth@EU's incident aggregation system. Limitations reflect the immaturity of current infrastructures and regulations, with real-world validation pending the rollout of key dependencies.

Indexed as

Artificial IntelligenceDecision Support Systems, ClinicalGovernment RegulationGuideline AdherenceDecision Support TechniquesEuropean UnionHealth Information InteroperabilityMetadataWorkflowAI ActcomplianceFast Healthcare Interoperability Resourcesgenerative AIHL7 CDAHL7 FHIRinteroperabilityMyHealth@EUOpenNCPreliabilitytrustworthiness

Identifiers

PMID41213096
PMCPMC12599979

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.