Quality of Conventional versus Artificial Intelligence Oral Surgery Consent Forms: Comparative Analysis.

Jan Gaessler, Bernhard Remschmidt, Ann-Kathrin Jopp, Behrouz Arefnia, Adrian Franke, Marcus Rieder

Abstract readComparative Study

In one paragraph

Article in Journal of medical Internet research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

1 citing paper in PubMed.

A Locally Executable AI System for Improving Preoperative Patient Communication: Multidomain Clinical Evaluation.JMIR medical informatics · 2026
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

6 authors.

Jan GaesslerDivision of Oral and Maxillofacial Surgery, Department of Dental Medicine and Oral Health, Medical University of Graz, Auenbruggerplatz 5/6, Graz, 8032, Austria, 43 316 385 83500.ORCID http://orcid.org/0000-0002-8728-8662

Bernhard RemschmidtDivision of Oral and Maxillofacial Surgery, Department of Dental Medicine and Oral Health, Medical University of Graz, Auenbruggerplatz 5/6, Graz, 8032, Austria, 43 316 385 83500.ORCID http://orcid.org/0000-0002-0408-5991

Ann-Kathrin JoppClinical psychologist and psychotherapist in private practice, Hannover, Germany.ORCID http://orcid.org/0000-0002-2281-0034

Behrouz ArefniaDivision of Restorative Dentistry, Periodontology and Prosthodontics, Department of Dental Medicine and Oral Health, Medical University of Graz, Graz, Austria.ORCID http://orcid.org/0000-0002-5685-3796

Adrian FrankeDepartment of Oral and Maxillofacial Surgery, University Hospital Carl Gustav Carus, Dresden University of Technology, Dresden, Germany.ORCID http://orcid.org/0000-0001-9324-9574

Marcus RiederDivision of Oral and Maxillofacial Surgery, Department of Dental Medicine and Oral Health, Medical University of Graz, Auenbruggerplatz 5/6, Graz, 8032, Austria, 43 316 385 83500.ORCID http://orcid.org/0000-0003-4412-9589

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Unlabelled: Artificial intelligence-generated informed consent forms for oral surgery demonstrated higher quality and better readability than conventional web-based forms, though both fell short of recommended comprehension levels.

Indexed as

Artificial IntelligenceConsent FormsInformed ConsentSurgery, OralComprehensionHumansAIartificial intelligenceconsent formdental healthdentistryinformed consentoral surgeonoral surgeryoral surgical procedurespaitent autonomypatient carequality control

Identifiers

PMID41490384

DOI10.2196/59851

PMCPMC12768391

What OpenQuestion holds

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LicenceCC BY

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Registered trials

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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.