Evidence map›Paper›PMID 42281346›Full record

ArticleJournal of oral pathology & medicine : official publication of the International Association of Oral Pathologists and the American Academy of Oral Pathology2026

Bridging the Health Literacy Gap for Patients With Oral Cancer: Readability Enhancement With AI Chatbots.

Agata Baczynska, Umar Rehman, Shireen S Gohari, Kelly Chu, Mohammad Sohaib Sarwar, Peter A Brennan

Abstract read
In one paragraph

Article in Journal of oral pathology & medicine : official publication of the International Association of Oral Pathologists and the American Academy of Oral Pathology, 2026. 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. Article
  2. Article
  3. Bridging the Health Literacy Gap for Patients With Oral Cancer: Readability Enhancement With AI Chatbots.Journal of oral pathology & medicine : official publication of the International Association of Oral Pathologists and the American Academy of Oral Pathology · 2026
    Article
  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

6 authors.

Agata BaczynskaImperial College London, London, UK.ORCID https://orcid.org/0009-0004-4459-5576
Umar RehmanUCL Division of Surgery and Interventional Science, UCL, London, UK.ORCID https://orcid.org/0000-0002-4856-3414
Shireen S GohariDepartment of Otolaryngology and Head and Neck Surgery, St George's University Hospital NHS Foundation Trust, London, UK.ORCID https://orcid.org/0000-0001-9166-3579
Kelly ChuDepartment of Plastic Surgery, Lister Hospital, East & North Hertfordshire NHS Trust, Stevenage, UK.
Mohammad Sohaib SarwarDepartment of Oral and Maxillofacial Surgery, Bradford Royal Infirmary, Bradford, West Yorkshire, UK.
Peter A BrennanDepartment of Oral and Maxillofacial Surgery, Queen Alexandra Hospital, Portsmouth, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEarly diagnosis is crucial in improving oral cancer outcomes. Patient education materials support timely recognition and management. However, these resources are often written above recommended reading levels, beyond patients' health literacy and limiting accessibility.

objectivesTo assess the readability of available patient information on oral cancer by the NHS, to evaluate three large language models (LLMs; ChatGPT, Claude and Gemini) in simplifying texts while preserving their content, and to propose an improved leaflet based on UK materials, expert review and LLM adjustment to match average UK reading levels.

methodsMaterials were collected from NHS-affiliated websites. Original and LLM-simplified texts were assessed using validated readability tools (FRES, FKGL, GFI, CLI and SMOG). Content fidelity was assessed using character 3-5-g cosine, sentence-content retention and latent semantic analysis (LSA). An expert review was applied to the proposed leaflet.

resultsLLM-revisions significantly improved readability across all five indices (p < 0.0001). Mean FRES of original texts was 66.4 ± 7.7, while Claude (81.6 ± 6.2) was the only model to surpass the 80 benchmark. Semantic similarity to source text remained high (LSA means 0.97 ± 0.04, 0.94 ± 0.09 and 0.96 ± 0.08; character 3-5-g cosine 0.85 ± 0.05, 0.80 ± 0.08 and 0.82 ± 0.08 for respective models). Baseline readability of the proposed leaflet was comparable to NHS materials (FRES 65.7); Claude increased this to 81.2.

conclusionsLLM-based simplification enhanced readability while preserving content fidelity. This approach can help enhance accessibility, particularly for populations disproportionately affected by oral cancer. With human oversight, it could be adopted at the policy level to standardise patient education and reduce health literacy disparities.

Indexed as

ComprehensionHealth LiteracyMouth NeoplasmsPatient Education as TopicGenerative Artificial IntelligenceHumansLarge Language ModelsReadingUnited Kingdomcontent fidelityhealth literacylarge language modelsmedical educationNational Health Service (UK)oral cancerpatient informationreadability

Identifiers

PMID42281346
PMCPMC13533865

What OpenQuestion holds

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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.