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Abstract

Assessing Lexical Diversity in Pharmaceutical Leaflets Using Type-Token Ratio for AI-Generated Inclusive Video Leaflets †

by
Yuri M. C. Luz
* and
Bianca F. O. Camargo
Flug–Startup for Digital Leaflets Innovation, Sorocaba 18023-000, SP, Brazil
*
Author to whom correspondence should be addressed.
†
Presented at the 6th International Congress on Health Innovation—INOVATEC 2025, Hybrid, 21–23 November 2025.
Proceedings 2026, 137(1), 148; https://doi.org/10.3390/proceedings2026137148
Published: 10 April 2026
(This article belongs to the Proceedings of The 6th International Congress on Health Innovation—INOVATEC 2025)
Versions Notes

Introduction: Low health literacy limits patient understanding of pharmaceutical leaflets. Artificial intelligence (AI)-generated video leaflets offer an inclusive solution, but the linguistic quality of the source texts plays a critical role in ensuring clarity, fluency, and accessibility. This study aimed to assess the relationship between lexical diversity in pharmaceutical leaflets and the effectiveness of AI-generated inclusive video leaflets. Methodology: A case study was conducted using pharmaceutical leaflets, which were analyzed for lexical richness through the Type-Token Ratio (TTR). These texts served as input for AI systems that generated video leaflets. The outputs were evaluated using natural language processing metrics and expert reviews to assess clarity, coherence, and inclusivity. Results: Higher TTR in pharmaceutical leaflets was associated with AI-generated videos showing improved clarity, coherence, and fluency. Richer vocabulary reduced semantic errors and enhanced inclusivity, especially for patients with low health literacy. Conclusions: Lexical richness measured by TTR directly improves the quality of AI-generated video leaflets, supporting patient understanding, regulatory compliance, and scalable digital health solutions. These findings highlight the importance of investing in high-quality written materials to maximize the impact of inclusive AI applications in healthcare.

Author Contributions

Conceptualization, Y.M.C.L.; methodology, Y.M.C.L.; software, Y.M.C.L.; validation, Y.M.C.L. and B.F.O.C.; formal analysis, Y.M.C.L.; investigation, Y.M.C.L.; resources, Y.M.C.L.; data curation, Y.M.C.L.; writing—original draft preparation, Y.M.C.L.; writing—review and editing, Y.M.C.L. and B.F.O.C.; visualization, Y.M.C.L.; supervision, Y.M.C.L.; project administration, Y.M.C.L.; funding acquisition, Y.M.C.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was carried out at UNISO Tech Hub. No external funding was received.

Institutional Review Board Statement

Not Applicable.

Informed Consent Statement

Not Applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to proprietary and confidentiality restrictions.

Conflicts of Interest

The authors declare that they are employed by Flug–Startup for Digital Leaflets Innovation. The company had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Luz, Y.M.C.; Camargo, B.F.O. Assessing Lexical Diversity in Pharmaceutical Leaflets Using Type-Token Ratio for AI-Generated Inclusive Video Leaflets. Proceedings 2026, 137, 148. https://doi.org/10.3390/proceedings2026137148

AMA Style

Luz YMC, Camargo BFO. Assessing Lexical Diversity in Pharmaceutical Leaflets Using Type-Token Ratio for AI-Generated Inclusive Video Leaflets. Proceedings. 2026; 137(1):148. https://doi.org/10.3390/proceedings2026137148

Chicago/Turabian Style

Luz, Yuri M. C., and Bianca F. O. Camargo. 2026. "Assessing Lexical Diversity in Pharmaceutical Leaflets Using Type-Token Ratio for AI-Generated Inclusive Video Leaflets" Proceedings 137, no. 1: 148. https://doi.org/10.3390/proceedings2026137148

APA Style

Luz, Y. M. C., & Camargo, B. F. O. (2026). Assessing Lexical Diversity in Pharmaceutical Leaflets Using Type-Token Ratio for AI-Generated Inclusive Video Leaflets. Proceedings, 137(1), 148. https://doi.org/10.3390/proceedings2026137148

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