ArticleBreast cancer research and treatment2025
Readability analysis of breast cancer resources shared on X-implications for patient education and the potential of AI.
Article in Breast cancer research and treatment, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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.
Who cites it
2 citing papers in PubMed.
- Evaluating the Performance of Large Language Models for Breast Cancer Patient Education: A Comparative Study.Journal of cancer education : the official journal of the American Association for Cancer Education · 2026Article
- Factors associated with the experience of AI tools for creating health education materials: cross-sectional study using an extended UTAUT model.BMC medical education · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeBreast cancer remains a global public health burden. This study aimed to evaluate the readability of breast cancer articles shared on X (formerly Twitter) during Breast Cancer Awareness Month (October 2024), and it explores the possibility of using artificial intelligence (AI) to improve readability.
methodsWe identified the top articles (n = 377) from posts containing #breastcancer on X during October 2024. Each article was analyzed using 9 established readability tests: Automated Readability Index (ARI), Coleman-Liau, Flesch-Kincaid, Flesch Reading Ease, FORCAST Readability Formula, Fry Graph, Gunning Fog Index, Raygor Readability Estimate, and Simple Measure of Gobbledygook (SMOG) Readability Formula. The study categorized sharing entities into five groups: academic medical centers, healthcare providers, government institutions, scientific journals, and all others. This comprehensive approach aimed to evaluate the readability of breast cancer articles across various sources during a critical awareness period of peak public engagement. A pilot study was simultaneously conducted using AI to improve readability. Statistical analysis was performed using SPSS.
resultsA total of 377 articles shared by the following entities were analyzed: academic medical centers (35, 9.3%), healthcare providers (57, 15.2%), government institutions (21, 5.6%), scientific journals (63, 16.8%), and all others (199, 53.1%). Government institutions shared articles with the lowest average readability grade level (12.71 ± 0.79). Scientific journals (16.57 ± 0.09), healthcare providers (15.49 ± 0.32), all others (14.89 ± 0.17), and academic medical centers (13.56 ± 0.39) had higher average readability grade levels. Article types were also split into different categories: patient education (222, 58.9%), open-access journal (119, 31.5%), and full journal (37, 9.6%). Patient education articles (15.19 ± 0.17) had the lowest average readability grade level. Open-access and full journals had an average readability grade level of 16.65 ± 0.06 and 16.53 ± 0.10, respectively. The mean values for Flesch Reading Ease Score are patient education 38.14 ± 1.2, open-access journals 16.14 ± 0.89, full journals 17.69 ± 2.14. Of note, lower readability grade levels indicate easier-to-read text, while higher Flesch Reading Ease scores indicate more ease of reading. In a demonstration using AI to improve readability grade level of 5 sample articles, AI successfully lowered the average readability grade level from 12.58 ± 0.83 to 6.56 ± 0.28 (p < 0.001).
conclusionsOur findings highlight a critical gap between the recommended and actual readability levels of breast cancer information shared on a popular social media platform. While some institutions are producing more accessible content, there is a pressing need for standardization and improvement across all sources. To address this issue, sources may consider leveraging AI technology as a potential tool for creating patient resources with appropriate readability levels.
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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.