European primary care is multilingual in practice. A GP in Brussels may document in Dutch while consulting with a patient speaking Moroccan Darija. A family doctor in Vienna switches between standard German and the Viennese dialect mid-sentence. A practice in Manchester sees patients whose first language is Urdu, Polish, or Somali. When AI documentation tools enter these environments, they encounter a linguistic reality that most were not designed for, and the performance gaps that follow are not minor inconveniences. They are potential patient safety risks.

How AI documentation tools process spoken language

To understand why performance varies across languages, it helps to know where the processing actually happens. Most AI documentation tools used in primary care combine two distinct components: automatic speech recognition (ASR), which converts spoken words into text, and a large language model (LLM) or natural language processing (NLP) layer, which transforms that transcribed text into structured clinical documentation.

Errors compound across both layers. If the ASR layer mishears a spoken word, particularly a clinical term pronounced with a regional accent, the NLP layer receives corrupted input and may generate a plausible-sounding but clinically incorrect note. Research on voice documentation systems has found that even specialty-specific speech recognition engines achieve limited diagnostic term accuracy within a single language, illustrating how domain-specific vocabulary creates accuracy gaps that become far more pronounced when language resources are scarce. Clinicians evaluating AI documentation tools should therefore ask not just "does it support this language?" but "where in the pipeline does it fail, and how?"

Why some European languages are better supported than others

The fundamental reason for performance disparities across European languages is training data imbalance. Large language models and ASR systems are predominantly trained on English-language datasets. When a model has seen billions of English clinical documents but only millions, or hundreds of thousands, of equivalent texts in Dutch, Romanian, or Greek, its performance in those languages will be structurally weaker.

Research published in Scientific Reports in 2025 addressed challenges for foundational LLMs in domain-specific tasks such as medical summarisation, including considerations of morphological richness, syntactic variation, and diglossia, with particular impact on underrepresented languages.

Languages that tend to be better supported include:

• English — by a substantial margin, due to dominant representation in training corpora

• Spanish, French, German — reasonably represented, though with gaps in clinical vocabulary

• Dutch, Portuguese, Italian — moderate support, with notable gaps in specialist terminology

Languages that are typically underrepresented in clinical AI training data include Polish, Romanian, Greek, Czech, Hungarian, Finnish, Catalan, Welsh, and Maltese. For clinicians practising in these languages, the baseline accuracy of any AI documentation tool should be independently verified, not assumed.

The specific challenges of Germanic, Romance, and Slavic languages in clinical documentation

Language family structure creates predictable failure modes in AI clinical documentation. Understanding these helps clinicians anticipate where errors are most likely to occur.

Germanic languages (German, Dutch)

German and Dutch make extensive use of compound nouns, single words built by joining multiple concepts. A German clinical term such as Herzinsuffizienz (heart failure) or Bluthochdruck (hypertension) must be recognised as a single clinical entity, not parsed as disconnected syllables. AI tools not trained on sufficient German-language clinical text frequently segment or misrecognise these compounds, generating notes that omit or distort the diagnosis.

Romance languages (French, Spanish, Portuguese, Italian)

These languages assign grammatical gender to medical terminology, and clinical meaning can shift with agreement errors. Beyond grammar, regional variation in clinical vocabulary is significant: the same condition may be described with different preferred terms in France versus Belgium, or in Spain versus Latin America. An AI tool trained on Castilian Spanish clinical data may underperform in Catalan-speaking regions, as demonstrated by research on bilingual Spanish and Catalan primary care notes, which found that joint recognition and ICD-10 linking of diagnoses in non-standard bilingual notes is a distinct and challenging problem requiring language-specific fine-tuning.

Slavic languages (Polish, Czech, Slovak)

Polish and Czech are morphologically complex, with extensive inflectional systems that change word endings based on grammatical case, gender, and number. A clinical term for a condition may appear in six or more forms within a single consultation, and an AI model without adequate exposure to this inflectional variation will fail to consistently recognise the same clinical concept across its forms. Multilingual trustworthiness evaluations of LLMs in healthcare have identified this as a critical barrier to real-world adoption in Slavic-language clinical environments.

Dialects, regional variation, and accented speech: the layer most tools ignore

Even within a single officially supported language, dialect variation and accented speech can substantially degrade ASR accuracy. A tool validated for standard Dutch (as spoken in the Netherlands) may still underperform in a Flemish GP practice in Ghent. Swiss German is sufficiently distinct from standard German that many ASR systems trained on Hochdeutsch fail to reliably transcribe it. Catalan, though spoken by millions across Spain and France, is frequently treated as an edge case by AI vendors whose primary market is Castilian Spanish.

A narrative review from Dublin City University's ADAPT Centre identifies this as one of the central unresolved challenges in AI language technology for healthcare: fluent output in a standard language variety does not guarantee acceptable performance across the full dialect continuum of that language. The review notes that efficiency gains from AI language tools can hide errors, reduce traceability, and shift responsibility across clinicians and health systems, risks that are amplified when dialect variation is not accounted for in validation.

Accented speech from non-native speakers presents a related but distinct challenge. A Romanian-born GP practising in Ireland and documenting in English with a Romanian accent may find that ASR accuracy is meaningfully lower than for a native English speaker using the same tool. This has direct implications for practices with internationally trained clinicians, which represent a significant proportion of primary care workforces across the EU and UK.

Code-switching: what happens when clinicians and patients mix languages mid-consultation

Code-switching, moving between two or more languages within a single conversation, is routine in multilingual clinical settings, yet it remains one of the most poorly handled scenarios in AI documentation tools. A clinician in Luxembourg may document in French while using Latin anatomical terms, English drug names, and occasional German phrases. A GP in a Welsh-speaking practice may alternate between Welsh and English within a single sentence.

Physicians in Arabic-speaking environments often converse mainly in Arabic but write clinical notes in English, adding cognitive load. This bilingual workflow is poorly supported by existing AI tools due to scarce Arabic-language training corpora. The same structural problem applies to any language pair where one component is underrepresented in training data.

For most current AI documentation tools, code-switching between a well-resourced and an under-resourced language tends to produce one of two failure modes: the tool defaults entirely to the dominant language and drops content spoken in the minority language, or it attempts to transcribe both languages but introduces systematic errors at the transition points. Neither outcome is acceptable in a clinical documentation context where missed or distorted information can affect patient safety.

Clinical terminology across languages: more than a translation problem

A common assumption is that multilingual clinical documentation is primarily a translation challenge, that an AI tool simply needs to map spoken terms in one language to their English equivalents before applying standard clinical coding. This assumption is incorrect, and acting on it leads to systematic errors in structured notes.

Medical vocabulary is not uniformly standardised across European languages. SNOMED CT, the most widely used clinical terminology system, has official translations in several European languages, but coverage is uneven. Clinicians in practice frequently use informal, abbreviated, or locally preferred terms that do not map directly to any standardised code. An AI tool trained on English clinical corpora may correctly recognise the spoken English term "heart failure" and map it to the appropriate SNOMED CT code, but fail to perform the same mapping when the term is spoken in Polish, Greek, or Finnish, even if the tool nominally "supports" those languages.

Research on ICD-10 coding in bilingual Spanish and Catalan primary care notes found that non-standard note formats and bilingual mixing create specific challenges for automated coding that cannot be resolved by applying models trained on standard monolingual corpora. The authors found that parameter-efficient fine-tuning on language-specific clinical data was necessary to achieve acceptable performance, a finding with direct implications for practices evaluating AI documentation tools in any non-English European language.

How to evaluate an AI documentation tool's language performance before deploying in practice

Clinicians and practice managers evaluating AI documentation tools for multilingual environments should go beyond vendor marketing claims and ask specific, verifiable questions. The following framework reflects current best practice in clinical AI evaluation.

Ask for language-specific validation data

• In which languages was the tool validated, and on what dataset?

• Was validation performed on real-world clinical speech or clean studio recordings?

• What was the word error rate (WER) for ASR in the target language, and how does this compare to English performance on the same tool?

Probe dialect and accent coverage

• Has the tool been tested on the specific regional variety of the language used in your practice (e.g., Flemish Dutch, Swiss German, Catalan)?

• What is the documented performance difference between standard and regional varieties?

Test code-switching capability

• Does the tool handle consultations where the clinician and patient use different languages?

• How does it behave when medical terms are spoken in Latin or English within a non-English consultation?

Review clinical coding accuracy separately from transcription accuracy

• A tool may achieve acceptable transcription accuracy while still failing to generate correct SNOMED CT or ICD codes in the target language

• Ask vendors for coding accuracy data specific to your language and clinical context

The 2025 commentary on AI scribes in healthcare notes that most existing evaluations come from small-scale, short-term pilot studies with participants biased toward technology, a limitation that applies with particular force to non-English language evaluations, where the evidence base is thinner still.

Data residency and regulatory considerations for multilingual AI tools in the EU

The General Data Protection Regulation (GDPR) applies to all personal data processed within the EU, regardless of the language in which it was spoken or recorded. Audio recordings of clinical consultations, including those conducted in Polish, Romanian, Arabic, or any other language, constitute sensitive health data under Article 9 of GDPR and are subject to the full range of data protection obligations.

A BMJ policy paper on AI translation in healthcare identifies the gap between rapidly accelerating AI deployment and regulatory frameworks as a significant concern, noting that this gap is particularly pronounced in multilingual healthcare settings where data flows across language and jurisdictional boundaries.

Practices should verify:

• Where audio data is processed: Some AI documentation tools route audio to cloud infrastructure outside the EU for transcription, which may conflict with GDPR data residency requirements

• Where data is stored: EU data residency requirements apply to stored data as well as processing

• Whether the vendor's privacy documentation covers all supported languages: Tools that process non-English audio through different infrastructure than English audio may have inconsistent data residency postures

• Medical Device Regulation (MDR) status: AI documentation tools that generate clinical outputs may qualify as medical devices under EU MDR, with implications for which languages and clinical contexts have been formally validated

What good multilingual performance actually looks like: benchmarks and red flags

There are no universally agreed accuracy thresholds for AI clinical documentation across European languages, but the following benchmarks reflect current evidence and clinical risk considerations.

Reasonable minimum thresholds for clinical use

• ASR word error rate below 10–15% for the specific language and dialect in use (lower thresholds apply for high-stakes clinical contexts)

• Clinical terminology recognition accuracy above 80% for the most common diagnostic terms in the target language

• ICD/SNOMED coding accuracy comparable to that achieved by the same tool in English

Red flags suggesting inadequate multilingual validation

• The vendor cites only English-language validation studies and describes other language support as "coming soon" or "in beta"

• Accuracy figures are presented as a single number across all supported languages, without language-specific breakdown

• Validation was performed on clean recordings rather than real-world clinical speech

• The tool has no documented performance data for regional dialects or accented speech

• Code-switching capability is described qualitatively rather than supported by accuracy data

The EuropeMedQA benchmark is a useful reference point: it is a comprehensive multilingual medical examination dataset sourced from official regulatory exams across European countries, and it provides a structured framework for comparing LLM performance across European clinical languages. Clinicians should be aware, however, that performance on standardised examination questions does not necessarily predict performance on real-world clinical speech. The two tasks involve different linguistic registers and error types.

What needs to change in AI clinical documentation for multilingual Europe

The multilingual performance gap in AI clinical documentation is not an intractable problem, but the research community and commercial vendors currently underserve it. Several changes are needed before AI documentation tools can be considered reliably safe for deployment across the full linguistic diversity of European primary care.

More diverse training datasets

The dominance of English-language data in AI training corpora reflects historical research and commercial priorities, not the actual distribution of clinical activity in Europe. Building clinically validated datasets in Polish, Romanian, Greek, Dutch, and other underrepresented languages requires investment from health systems, research funders, and AI vendors. The ADAPT Centre's 2026 review argues that this requires not only better models but accountable sociotechnical design and stronger collaboration across natural language processing, clinical practice, and policy.

Dialect-aware model development

Standard language varieties are insufficient as the basis for clinical AI validation. Models need to be tested and, where necessary, fine-tuned on the regional varieties actually used in clinical practice, including Flemish Dutch, Swiss German, Catalan, regional French accents, and the many other varieties that constitute the real linguistic landscape of European primary care.

Clinical validation as a regulatory requirement

The BMJ policy paper calls for evidence-informed policy frameworks that require AI language tools in healthcare to demonstrate clinical safety across the languages and contexts in which they are deployed. Without regulatory pressure, vendors have limited commercial incentive to invest in validation for smaller language markets.

Honest representation of current limitations

The evidence from multilingual LLM trustworthiness research is clear: current models are not uniformly reliable across European languages in clinical settings. Clinicians deserve accurate information about where these tools perform well and where they do not, so they can apply appropriate human oversight and avoid over-reliance on AI-generated documentation in languages where validation is absent or inadequate.

For clinicians practising in multilingual European environments today, the practical implication is straightforward: language support listed on a vendor's website is not the same as validated clinical performance. The questions to ask, the benchmarks to request, and the red flags to watch for are well-defined. Applying them rigorously before deployment is the most reliable protection against the compounding errors that multilingual AI documentation tools can introduce into clinical records.

Frequently asked questions

▶ Why do AI documentation tools perform differently across European languages?

The core reason is training data imbalance. Large language models and automatic speech recognition systems are predominantly trained on English-language datasets. A model trained on billions of English clinical documents but only hundreds of thousands of equivalent texts in Romanian or Greek will be structurally weaker in those languages. This affects both the transcription layer and the layer that converts transcribed text into structured clinical notes.

▶ Which European languages are best and least supported by clinical AI documentation tools?

English is the best-supported language by a substantial margin. Spanish, French, and German have reasonable representation, though with gaps in clinical vocabulary. Dutch, Portuguese, and Italian have moderate support. Languages that are typically underrepresented include Polish, Romanian, Greek, Czech, Hungarian, Finnish, Catalan, Welsh, and Maltese. Clinicians practising in these languages should independently verify baseline accuracy rather than assume it.

▶ What specific documentation errors should clinicians expect with Germanic and Slavic languages?

In German and Dutch, AI tools frequently misrecognise compound nouns such as Herzinsuffizienz (heart failure), either segmenting or omitting them entirely. In Polish and Czech, extensive inflectional systems mean the same clinical term can appear in six or more forms within a single consultation. Tools without adequate exposure to this variation will fail to consistently recognise the same clinical concept across its different forms, which multilingual trustworthiness evaluations of large language models in healthcare have identified as a critical barrier to real-world adoption.

▶ Does dialect and accented speech affect AI documentation accuracy?

Yes, significantly. A tool validated for standard Dutch may still underperform in a Flemish practice. Swiss German is sufficiently distinct from standard German that many speech recognition systems trained on Hochdeutsch fail to reliably transcribe it. Accented speech from non-native speakers presents a related challenge: a Romanian-born GP documenting in English may find that transcription accuracy is meaningfully lower than for a native English speaker using the same tool. Research from Dublin City University's ADAPT Centre identifies dialect variation as one of the central unresolved challenges in AI language technology for healthcare.

▶ How do AI documentation tools handle code-switching, where clinicians mix languages mid-consultation?

Most current tools handle code-switching poorly. When a clinician moves between a well-resourced and an under-resourced language, tools typically either default entirely to the dominant language and drop content spoken in the minority language, or attempt to transcribe both but introduce systematic errors at the transition points. Neither outcome is acceptable in clinical documentation, where missed or distorted information can affect patient safety.

▶ Is multilingual clinical documentation just a translation problem?

No. Medical vocabulary is not uniformly standardised across European languages. SNOMED CT, the most widely used clinical terminology system, has official translations in several European languages, but coverage is uneven. Clinicians frequently use informal or locally preferred terms that don't map directly to any standardised code. Research on ICD-10 coding in bilingual Spanish and Catalan primary care notes found that non-standard note formats and bilingual mixing create specific challenges that cannot be resolved by applying models trained on standard monolingual corpora.

▶ What questions should clinicians ask vendors when evaluating an AI documentation tool for a multilingual practice?

Clinicians should ask for language-specific validation data, including word error rate for automatic speech recognition in the target language compared to English. They should ask whether the tool has been tested on the specific regional variety used in their practice, such as Flemish Dutch or Swiss German. They should also probe how the tool handles code-switching, and request clinical coding accuracy data specific to their language and context, since a tool may achieve acceptable transcription accuracy while still failing to generate correct SNOMED CT or ICD codes in the target language.

▶ What are the GDPR implications of using AI documentation tools that process non-English audio?

Audio recordings of clinical consultations in any language constitute sensitive health data under Article 9 of the General Data Protection Regulation and carry the full range of data protection obligations. Practices should verify where audio data is processed and stored, since some tools route audio to cloud infrastructure outside the EU for transcription. Tools that process non-English audio through different infrastructure than English audio may have inconsistent data residency postures. Medical Device Regulation status is also relevant, since AI documentation tools that generate clinical outputs may qualify as medical devices, with implications for which languages and clinical contexts have been formally validated.

▶ What accuracy benchmarks indicate an AI documentation tool is suitable for clinical use in a non-English language?

The article sets out the following minimum thresholds based on current evidence: an automatic speech recognition word error rate below 10 to 15 per cent for the specific language and dialect in use, clinical terminology recognition accuracy above 80 per cent for the most common diagnostic terms in the target language, and ICD or SNOMED coding accuracy comparable to that achieved by the same tool in English. Red flags include vendors citing only English-language validation studies, presenting accuracy as a single figure across all supported languages, and describing dialect or code-switching performance qualitatively rather than with accuracy data.

▶ What changes are needed before AI documentation tools can be considered reliably safe across multilingual European primary care?

The article identifies three main requirements. First, more diverse training datasets in underrepresented languages such as Polish, Romanian, and Greek. Second, dialect-aware model development that goes beyond standard language varieties to cover regional varieties actually used in clinical practice. Third, clinical validation as a regulatory requirement, so that vendors must demonstrate safety across the languages and contexts in which their tools are deployed. Without regulatory pressure, vendors have limited commercial incentive to invest in validation for smaller language markets.