LLM-Assisted Review Prioritization for German Statutory Health Insurance Websites: A Multi-Stage Corpus Audit

Martin M\"oller

REVIEW 2 major objections 6 minor 13 references

LLM-Assisted Review Prioritization for German Statutory Health Insurance Websites: A Multi-Stage Corpus Audit

T0 review · 2 major / 6 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read This paper claims that a staged, evidence-preserving audit can convert the entire public web portfolio of German statutory health insurers into a prioritized, human-reviewed workload without mistaking AI-provenance signals for content error

desk verdict A disciplined, honestly-limited LLM-assisted audit workflow for a 56k-page corpus; the workload counts are defensible as model outputs, but 'review demand' still needs a human anchor. read the letter →

arxiv 2608.03500 v1 pith:RGIEOGIR submitted 2026-08-04 cs.CY

Martin Möller This is my paper · ORCID

classification cs.CY

keywords statutoryhealthinsurancewebsitecorpusauditLLM-assistedreviewprioritizationprovenanceversusqualitysignalspubliccommunicationhumanadjudicationGermanweb

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper asks whether the public websites of German statutory health insurers—56,198 pages across 84 sites—can be processed into a structured backlog that human specialists can actually review. Its claim is that they can, if the work is organized as a ladder: deterministic screening, model-assisted triage, in-depth review, minimum evidence checks, recurring-pattern grouping, and a final human-adjudication boundary. The workflow produced 35,998 review records, routed 21,452 into a case-review queue, and located a quoted passage in captured page text for 31,347 of them. It deliberately refuses to call these counts errors or prevalence: literal occurrence is not factual correctness, and two models agreeing (75.8%, kappa 0.532) measures consistency, not truth. The point of caring is that compliance-style auditing of public health communication may be feasible at scale without pretending models can adjudicate.

What carries the argument

A review record is the unit that carries the argument: a claim-like passage stored with page context, materiality, evidence status, and routing label, which must survive deterministic minimum evidence checks before entering aggregate results. It works with a P/Q split—provenance indicators (how text may have been produced) are kept apart from quality indicators (what may need medical, legal, or editorial review)—so an AI-style signal can route a page without being treated as a finding. Recurring-pattern grouping then maps records to shared concepts to expose cross-insurer review needs rather than isolated page claims.

What would settle it

Take a random sample of the 21,452 case-review records and have two independent human specialists adjudicate, with page context and official sources, whether each passage needs correction; if the actionable rate among flagged records is no higher than among a matched sample of unflagged pages, the prioritization is not doing its claimed work.

Watch

Extended reading notes

Core claim

The central claim is that the full public website portfolio of German statutory health insurers can be turned into a structured, evidence-preserving review workload at corpus scale while keeping production-provenance signals separate from substantive quality signals. The empirical support is a frozen snapshot: 56,198 pages from 84 site entities all received a recorded page state; 35,998 review records passed minimum evidence checks; 31,347 had a quoted passage located literally in captured page text; 290 recurring review patterns emerged; and 21,452 records were routed to human case review. The intended reading is deliberately narrow: literal occurrence is not correctness, the 300-page stres

Load-bearing premise

A model-generated flag counts as legitimate review demand once its quoted sentence appears literally in the captured page text, even though literal occurrence does not establish that the passage is misleading, untrue, or in need of change.

Editorial extensions

If this is right

Every page in a corpus audit can receive a recorded review state, making the audit backlog itself measurable; this snapshot shows closure at 56,198 pages with zero pages awaiting review.
Capacity planning becomes concrete: 35,998 review records, 21,452 case-review entries, and 290 recurring patterns define a specialist workload rather than an error count.
The P/Q split makes AI-provenance signals routable for explanation and prioritization without determining content correctness, so an AI-assisted page is not automatically suspect.
The temporal-validity safeguard turns post-cutoff legal and medical updates into routing triggers, addressing the failure mode where the page and the model share an outdated view.
Paired-model disagreement defines a bounded adjudication queue; agreement alone cannot close a case, keeping public claims behind human review with preserved context.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the evidence bar really is literal quote occurrence, then the value of the 21,452-case queue depends entirely on downstream human precision; a human-labeled sample of routed versus non-routed pages would be the natural next test.
The same pipeline shape—deterministic screen, cheap triage, in-depth review, evidence check, human gate—could transfer to other public-facing regulated content, such as financial advice or medication information, provided the reference-source layer is jurisdiction-specific.
The 300-page stress test's 33% signal on an enriched sample hints that the lower-priority bucket may hide a nontrivial residual workload; a random, non-enriched sample with human adjudication would quantify that hidden load.
The exploratory observability coding, which found clear public correction pathways in only 6 of 84 entities, suggests that even if the workflow correctly identifies review need, the websites themselves rarely document who is responsible for fixing content—making the adjudication backlog harder to close.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Dear colleague,

You should know two things about this paper before reading it. First, it is the rare LLM-applied paper that respects its own limits: it says outright that quote location confirms literal occurrence, not correctness, that kappa measures consistency, and that none of its calibration layers is a human reference standard. Second, the central workload numbers—35,998 review records, 21,452 routed to case review—are best read as capacity-planning outputs of a proprietary pipeline, not as measured review demand. The authors don't claim otherwise, but the distinction matters for how you interpret the results.

What's actually new is the integration: deterministic screening, lower-cost triage, in-depth LLM review, minimum evidence checks, temporal-validity triggers, and paired-model comparison applied to 56,198 German statutory health insurance pages, with a clear separation between provenance signals and substantive quality signals. The Reader Map is a model of disciplined claim-making; it fixes denominators and lists supported vs unsupported claims for every analytic level. The internal arithmetic is consistent, and the paper repeatedly refuses to convert workload into prevalence or model agreement into correctness. That is genuinely useful for anyone building similar content-governance workflows.

The soft spot is the one the stress-test note identifies: a review record's validity is anchored only on the quoted passage literally appearing in the captured page text. That means the 31,347 'located' records confirm existence in the text, not that the passage is materially problematic or that the model's concern is real. A queue of candidate passages may still be useful for human review, but until a human-adjudicated precision sample is published, the workload counts remain self-referential. The authors know this; they list a dated human anchor set as future work. The proprietary code, prompts, thresholds, and concept inventory further limit reproducibility, though the frozen derived tables at Zenodo at least allow the aggregate arithmetic to be checked.

The 300-page stress test and the kappa = 0.532 agreement are presented with appropriate caveats. The observability coding is explicitly exploratory and model-assisted.

Overall: this is a solid, honest applied paper, not a breakthrough. It deserves a serious referee, and I'd cite it for the P/Q decoupling and the evidence-boundary design. The missing human anchor keeps it from yet supporting operational claims about 'review demand.' Recommend peer review, with the expectation that the main revision will include an external precision check.

Referee Report

2 major / 6 minor

Summary. The manuscript reports a descriptive, implementation-oriented audit of 56,198 public web pages from 84 German statutory health insurance website entities. A proprietary multi-stage pipeline (deterministic screening, LLM triage, in-depth review, minimum evidence checks, temporal-validity triggers, paired-model comparison) assigned every page a review state and generated 35,998 review records, of which 31,347 had a quoted passage located in the captured page text and 21,452 were routed to a case-review queue. The paper carefully distinguishes workload and capacity claims from error-prevalence, legal, or AI-authorship claims; the Reader Map in §1.1 and the calibration table in §3.5 make these boundaries explicit. Secondary analyses include a 300-page routing stress test (33.3% within-sample signal) and a paired-model agreement analysis (P_o = 0.758, kappa = 0.532, 95% CI 0.415–0.649), both framed as consistency/calibration checks rather than validation. The stated conclusion is that the workflow produces a prioritized workload, not confirmed findings.

Significance. If the result holds, it demonstrates operational feasibility of evidence-preserving, model-assisted review prioritization for a large public health-information corpus, which is a real gap given manual specialist review capacity. The paper is unusually explicit about its evidence boundary: the Reader Map, the calibration table, and the limitations section prevent overreading of the counts as prevalence or correctness estimates. The descriptive arithmetic is internally consistent, including the concept/pattern reconciliation, the category-share calculations in §4.1.2, and the paired agreement matrix in §4.3.1, where kappa = 0.532 is correctly computed from P_o = 0.758 and P_e = 0.484. At the same time, the contribution is a single-operator, self-reported case study: no human reference standard anchors the workload labels, and the production code, prompts, thresholds, and raw page text are not publicly released. The central claim is therefore defensible only in the bounded sense of model-flagged candidate records and capacity demand, not as validated prioritization quality. The paper itself largely acknowledges this, which is a strength; the remaining problem is that the title and abs

major comments (2)

[Title, Abstract, RQ1 (§1), §5.1] The workload claim is defensible only under the 'candidate records / capacity demand' interpretation, because §3.5 explicitly states that none of the calibration layers is a human reference standard and §4.1.3 states that quote location confirms literal occurrence, not factual correctness. The title and abstract nevertheless say the workflow 'prioritizes substantive review needs.' Without a human-anchored precision check, prioritized review demand is not empirically distinguished from 'the model flagged these.' Please either (i) add a small human-adjudicated precision sample from the 21,452 case-review queue, or (ii) systematically replace 'review needs' / 'prioritization' with 'candidate review records' / 'capacity-demand estimates' in the title, abstract, RQ1, and Section 5.1.
[§3.7, §7.3–§7.4] The central numerical results (35,998 records; 31,347 located; 21,452 routed) cannot be independently recomputed because production code, prompts, thresholds, concept inventory, and captured page text are not released; the public package contains only frozen aggregate tables and the paired matrix. For a paper whose main result is a set of counts, this makes the empirical claim self-reported. Please release de-identified quote-level evidence-status records (or a random sample) and versioned summaries of the deterministic check rules, or explicitly reposition the paper as an internal audit case study without an independent reproducibility claim.

minor comments (6)

[§1.1 / Abstract] The phrase 'prioritizes substantive review needs' in the abstract and title is stronger than the supported claim in the Reader Map. Consider using 'candidate review records' consistently, especially in the Objective sentence.
[§4.1.3 vs §4.2.1] The number 20 appears both as 'missing quote status' review records and as '20 collection errors' in the lower-cost triage run. These are likely unrelated, but the identical number creates confusion. Clarify whether these are the same 20 or a coincidence.
[§3.6 / §7.8] The released dataset's model_use_summary.tsv is said to omit the public-observability coding and the primary stress-test reviewer, with the manuscript table 'authoritative.' This mismatch should be resolved by updating the dataset metadata or by explaining the discrepancy in the Data Availability note.
[§3.3 / §4.1.2] The broad category labels are English renderings of German schema codes (Transparenz, Recht, Medizin, Widerspruch, KI-Fail). Consider showing the German codes in parentheses when the categories are first introduced, since the German labels are the operational schema identifiers.
[Throughout] There are several typographical issues, including repeated 'sufficiently' rendered with a non-standard ligature ('suﬀiciently'). A careful proofreading pass is needed.
[References] The legal citations in Section 1 are listed in the order '2026d,e,b,c,f' rather than alphabetically or chronologically. Reorder for consistency with the reference list.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the workload claims are descriptive, explicitly bounded as model-generated evidence, and no prediction or first-principles result reduces to its inputs.

full rationale

The manuscript's strongest claim is feasibility: that a governed pipeline can turn a large public web corpus into a structured, evidence-carrying review workload. This is demonstrated by the pipeline's own execution, and the paper does not convert the resulting records into prevalence, correctness, or validation claims. The apparent self-referential concern—that the 35,998 review records are generated by the same model system that defines what counts as a review record—is explicitly disarmed. Section 3.2 defines a review record as 'a reviewable statement, claim-like passage, or page segment stored with context, evidence status, materiality fields, and counterargument fields' and explicitly excludes 'a confirmed error, page-level accusation, or public finding.' Section 4.1.3 states: 'Location confirms literal occurrence in the captured text, not factual correctness.' Section 3.5 states: 'None of these layers is a human reference-standard validation study.' The Reader Map restricts the supported claim for the 35,998 records to 'Stored passages require further assessment and define capacity demand' and explicitly denies 'Error prevalence or confirmed findings.' Thus the workload counts are internal workflow outputs, not independent truth claims; the paper does not predict anything from them. The 300-page stress test is labeled 'a single-model, risk-enriched routing stress test, not a human-reference evaluation,' and the kappa result is stated to 'quantify consistency, not correctness or sufficient triage performance.' There are no fitted parameters renamed as predictions, no imported uniqueness theorems, no load-bearing self-citations (the author does not appear among the cited authors), and no ansatz smuggled in via citation. The proprietary-code limitation affects reproducibility and is acknowledged in the Data and Code Availability sections; it is not circularity. The audit's inability to establish that stored records are genuine review needs is a construct-validity limitation that the paper repeatedly concedes, not a hidden equivalence between input and output.

Assumptions & free parameters 4 free parameters · 6 assumptions · 0 invented entities

The ledger reflects that the paper's numerical outputs are not derived from first principles; they are products of a proprietary pipeline with hand-set thresholds. The core assumptions are domain-level: captured text equals published text, literal quote occurrence is enough to count as evidence-linked workload, and model labels can stand in for prioritization without a human anchor. These are stated or conceded in the paper. No new physical or system-level entities are introduced.

free parameters (4)

In-depth review routing threshold = Not disclosed (proprietary)
Determines which 18,215 of 56,198 pages enter in-depth review; all downstream record counts depend on it (Sections 3.3 and 4.2.1).
Pattern recurrence threshold = Not disclosed (proprietary)
A record enters a recurring pattern only if it recurs across entities; the threshold shapes the 290 patterns and the non-disjoint assignment structure (Sections 3.3 and 4.1.1).
Tier assignment thresholds = Not disclosed (proprietary)
The 42 Tier-1, 31 Tier-2, and 217 Tier-3 assignments combine entity count, severity, statutory references, and quote counts with exact thresholds undisclosed (Section 4.1.1).
80-word minimum for stress-test pages = 80 words
Restricts the 300-page lower-priority sample; the 33.3% residual yield is conditional on this filtering (Section 3.5).

assumptions (6)

domain assumption Captured page text after crawler extraction and Markdown normalization faithfully represents the published web page
All quote-location checks and review evidence assume the captured page equals the page as published (Sections 3.1 and 3.3).
domain assumption A review record whose quoted passage occurs literally in captured page text passes the minimum evidence gate and can count as a review candidate
31,347 of 35,998 records are counted as evidence-linked on literal occurrence alone; the paper states this is not factual correctness (Section 4.1.3).
domain assumption LLM-generated labels, after deterministic checks, are a valid proxy for review prioritization without a human reference standard
All RQ1 and RQ2 workload counts rest on this; Section 3.5 concedes that none of the calibration layers is a human reference-standard validation.
domain assumption Crawler eligibility rules (sitemaps, path rules, minimum word count) define an audit denominator that is complete for the 84 entities
Pages failing crawler quality gates are outside the analytic denominator; coverage closure is closure over the constructed corpus, not over all SHI web content (Section 3.1).
standard math Cohen's kappa and Wilson confidence intervals are valid summaries for nominal agreement and binomial sample uncertainty
Used for paired-model agreement and the 300-page stress-test interval (Sections 3.5 and 4.2.2).
domain assumption The cited legal framework (SGB V, HWG, EU AI Act Article 50) provides the relevant review standards
Legal source-check patterns and temporal-validity triggers are grounded in these statutes (Introduction and Section 3.3).

how reviews work

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Cite this review

Pith. "Pith review of LLM-Assisted Review Prioritization for German Statutory Health Insurance Websites: A Multi-Stage Corpus Audit." pith.science (2026). https://pith.science/paper/RGIEOGIR

@misc{pith2026260803500,
  author       = {Pith},
  title        = {Pith review of: LLM-Assisted Review Prioritization for German Statutory Health Insurance Websites: A Multi-Stage Corpus Audit},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RGIEOGIR}},
  note         = {Machine review of arXiv:2608.03500}
}

read the original abstract

Background: German statutory health insurance (SHI) funds publish web portfolios that exceed continuous specialist review capacity. Their content can shape health and benefit expectations. Generic AI-text detection does not identify medical, benefit, legal, or editorial review needs. Objective: To characterize a multi-stage workflow that prioritizes substantive review needs while separating AI-provenance signals from quality claims. Methods: We analyzed 56,198 pages from 84 SHI websites or sub-sites. The workflow combined deterministic screening, model-assisted triage and in-depth review, minimum evidence checks, temporal-validity safeguards, and paired-model comparison. It is reproducibility-bounded, not a validated detector. Production code is proprietary; reproducibility rests on frozen derived tables and paired-comparison artifacts. The 300-page lower-priority check was a single-model, risk-enriched routing stress test, not a human-reference evaluation. Results: All pages received a review state. The workflow generated 35,998 review records and routed 21,452 to case review. The workload concentrated in transparency, legal framing, medical content, contradictions, and AI-related failure-mode signals. A quoted passage was locatable in captured page text for 31,347 records, confirming literal occurrence rather than factual correctness. The routing stress test surfaced a signal on 100/300 pages (33.3% within the sample). Across 182 matched cases, two models agreed in 75.8% (kappa = 0.532; 95% CI 0.415-0.649). Conclusions: The workflow produces a prioritized workload, not error prevalence or final legal, medical, or insurer-level findings. It neither proves AI authorship nor validates autonomous detection. Paired-model agreement quantifies consistency, not correctness or sufficient triage performance; public claims require human adjudication.

Figures

Figures reproduced from arXiv: 2608.03500 by the authors.

**Figure 2.** Broad review categories by materiality and page-quote-location profile. Horizontal [PITH_FULL_IMAGE:figures/full_fig_p015_2.png] view at source ↗

**Figure 3.** Review-record properties in the 2026-05-18 evidence base. Grouped bar chart of review [PITH_FULL_IMAGE:figures/full_fig_p017_3.png] view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: Page-level review-state coverage in the 2026-05-19 corpus coverage snapshot. Bar chart [PITH_FULL_IMAGE:figures/full_fig_p018_4.png]

**Figure 5.** Figure 5: Paired model agreement matrix. Confusion-matrix heatmap for 182 matched cases, with [PITH_FULL_IMAGE:figures/full_fig_p020_5.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

13 extracted references · 6 canonical work pages

[2]

URL https://www.nature.com/articles/s43856-024-00717-2

doi: 10.1038/s43856-024-00717-2. URL https://www.nature.com/articles/s43856-024-00717-2 . Wenting Chen, Guo Yu, Yiu-Fai Cheung, Meidan Ding, Jie Liu, Zizhan Ma, Wenxuan Wang, and Linlin Shen. Beyond the Leaderboard: Rethinking Medical Benchmarks for Large Language Models,

doi:10.1038/s43856-024-00717-2
[3]

Version 2 posted April 29, 2026; accepted by ACL

URL https://arxiv.org/abs/2508.04325. Version 2 posted April 29, 2026; accepted by ACL

arXiv 2026
[5]

Version 1 submitted April 14,

URL https://arxiv.org/abs/2604.26965 . Version 1 submitted April 14,

arXiv
[6]

European Parliament and Council of the European Union

URL https://digital-strategy.ec.europa.eu/en/policies/guidelines-transparency- ai-generated-content. European Parliament and Council of the European Union. Regulation (EU) 2024/1689 of 13 June 2024 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Oﬀicial Journal of the European Union,

work page 2024
[9]

URL https://www.frontier sin.org/journals/digital-health/articles/10.3389/fdgth.2026.1761641/full

doi: 10.3389/fdgth.2026.1761641. URL https://www.frontier sin.org/journals/digital-health/articles/10.3389/fdgth.2026.1761641/full. 30 National Center for Biotechnology Information. APIs - Develop - NCBI. NCBI,

work page arXiv 2026
[11]

URL https://www.monitor-versorgungsforschung.de/abstract/kuenstliche-intelligenz-und-gesundheit skompetenz-moeglichkeiten-und-grenzen-oeffentlich-zugaenglicher-ki-sprachmodelle/

doi: 10.24945/MVF.02.25.1866-0533.2707. URL https://www.monitor-versorgungsforschung.de/abstract/kuenstliche-intelligenz-und-gesundheit skompetenz-moeglichkeiten-und-grenzen-oeffentlich-zugaenglicher-ki-sprachmodelle/ . Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric P. Xing, Hao ...

work page arXiv
[12]

Version 4 revised December 24, 2023; NeurIPS 2023 Datasets and Benchmarks Track

URL https://arxiv.org/ab s/2306.05685 . Version 4 revised December 24, 2023; NeurIPS 2023 Datasets and Benchmarks Track. Richard Zowalla, Daniel Pfeifer, and Thomas Wetter. Readability and topics of the German Health Web: Exploratory study and text analysis. PLOS ONE , 18(2):e0281582,

arXiv 2023
[13]

URL https://journals.plos.org/plosone/article?id=10.1371/journal.pone.02 81582

doi: 10.1371/jo urnal.pone.0281582. URL https://journals.plos.org/plosone/article?id=10.1371/journal.pone.02 81582. 31

doi:10.1371/jo

Show all 13 references

[1969]

Gemeinsamer Bundesausschuss

doi: 10.1037/h0028106. Gemeinsamer Bundesausschuss. AIS - Maschinenlesbare Fassung der Beschluesse zur Nutzenbewer- tung von Arzneimitteln gemaess Section 35a SGB V. Gemeinsamer Bundesausschuss, 2026a. URL https://www.g-ba.de/themen/arzneimittel/arzneimittel-richtlinie-anlagen...

doi
[2023]

URL https://www.monitor-versorgungsforschung.d e/wp-content/uploads/2023/05/MVF0323_Scherenberg-Preuss.pdf

doi: 10.24945/MVF.03.23.1866-0533.2516. URL https://www.monitor-versorgungsforschung.d e/wp-content/uploads/2023/05/MVF0323_Scherenberg-Preuss.pdf. Viviane Scherenberg, Doreen Mueller, and Michael Erhart. Kuenstliche Intelligenz und Gesundheit- skompetenz: Moeglichkeiten und G...

2023
[2024]

Federal Ministry of Justice and Federal Oﬀice of Justice

URL https://eur-lex.europa.eu/eli/reg/2024/1689/oj. Federal Ministry of Justice and Federal Oﬀice of Justice. Gesetz ueber die Werbung auf dem Gebiete des Heilwesens (Heilmittelwerbegesetz - HWG), Section

2024
[2025]

URL https://jamanetwork.com/journals/jama/fullarticle/2825147

doi: 10.1001/jama.2024.21700. URL https://jamanetwork.com/journals/jama/fullarticle/2825147. Felix Busch, Lena Hoffmann, Christopher Rueger, et al. Current applications and challenges in large language models for patient care: a systematic review. Communications Medicine , 5:26,

2024
[2026]

Cochrane Help Centre

URL https://documentation.cochrane.org/spaces/API/pages/117377314/ReviewDB%2BAPI. Cochrane Help Centre. How can I access the Cochrane Library? Cochrane Help Centre,

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.

[1] [2]

URL https://www.nature.com/articles/s43856-024-00717-2

doi: 10.1038/s43856-024-00717-2. URL https://www.nature.com/articles/s43856-024-00717-2 . Wenting Chen, Guo Yu, Yiu-Fai Cheung, Meidan Ding, Jie Liu, Zizhan Ma, Wenxuan Wang, and Linlin Shen. Beyond the Leaderboard: Rethinking Medical Benchmarks for Large Language Models,

doi:10.1038/s43856-024-00717-2

[2] [3]

Version 2 posted April 29, 2026; accepted by ACL

URL https://arxiv.org/abs/2508.04325. Version 2 posted April 29, 2026; accepted by ACL

arXiv 2026

[3] [5]

Version 1 submitted April 14,

URL https://arxiv.org/abs/2604.26965 . Version 1 submitted April 14,

arXiv

[4] [6]

European Parliament and Council of the European Union

URL https://digital-strategy.ec.europa.eu/en/policies/guidelines-transparency- ai-generated-content. European Parliament and Council of the European Union. Regulation (EU) 2024/1689 of 13 June 2024 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Oﬀicial Journal of the European Union,

work page 2024

[5] [9]

URL https://www.frontier sin.org/journals/digital-health/articles/10.3389/fdgth.2026.1761641/full

doi: 10.3389/fdgth.2026.1761641. URL https://www.frontier sin.org/journals/digital-health/articles/10.3389/fdgth.2026.1761641/full. 30 National Center for Biotechnology Information. APIs - Develop - NCBI. NCBI,

work page arXiv 2026

[6] [11]

URL https://www.monitor-versorgungsforschung.de/abstract/kuenstliche-intelligenz-und-gesundheit skompetenz-moeglichkeiten-und-grenzen-oeffentlich-zugaenglicher-ki-sprachmodelle/

doi: 10.24945/MVF.02.25.1866-0533.2707. URL https://www.monitor-versorgungsforschung.de/abstract/kuenstliche-intelligenz-und-gesundheit skompetenz-moeglichkeiten-und-grenzen-oeffentlich-zugaenglicher-ki-sprachmodelle/ . Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric P. Xing, Hao ...

work page arXiv

[7] [12]

Version 4 revised December 24, 2023; NeurIPS 2023 Datasets and Benchmarks Track

URL https://arxiv.org/ab s/2306.05685 . Version 4 revised December 24, 2023; NeurIPS 2023 Datasets and Benchmarks Track. Richard Zowalla, Daniel Pfeifer, and Thomas Wetter. Readability and topics of the German Health Web: Exploratory study and text analysis. PLOS ONE , 18(2):e0281582,

arXiv 2023

[8] [13]

URL https://journals.plos.org/plosone/article?id=10.1371/journal.pone.02 81582

doi: 10.1371/jo urnal.pone.0281582. URL https://journals.plos.org/plosone/article?id=10.1371/journal.pone.02 81582. 31

doi:10.1371/jo