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Bioengineering
Volume 12
Issue 10
10.3390/bioengineering12101066
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

A Generative Expert-Narrated Simplification Model for Enhancing Health Literacy Among the Older Population

by
Akmalbek Abdusalomov
1,
Sabina Umirzakova
1,
Sanjar Mirzakhalilov
2,6,
Alpamis Kutlimuratov
3,
Rashid Nasimov
4,
Zavqiddin Temirov
5,
Wonjun Jeong
1,
Hyoungsun Choi
1 and
Taeg Keun Whangbo
1,*
1
Department of Computer Engineering, Gachon University, Sujeong-Gu, Seongnam-Si 13120, Gyeonggi-Do, Republic of Korea
2
Department of Computer Systems/Information and Educational Technologies, Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi, Tashkent 100200, Uzbekistan
3
Department of Applied Informatics, Kimyo International University in Tashkent, Tashkent 100121, Uzbekistan
4
Department of Artificial intelligence, Tashkent State University of Economics, Tashkent 100066, Uzbekistan
5
Department of Digital Technologies, Alfraganus University, Yukori Karakamish Street 2a, Tashkent 100190, Uzbekistan
6
Department of Information Processing and Management Systems, Tashkent State Technical University, Tashkent 100095, Uzbekistan
*
Author to whom correspondence should be addressed.
Bioengineering 2025, 12(10), 1066; https://doi.org/10.3390/bioengineering12101066
Submission received: 28 August 2025 / Revised: 26 September 2025 / Accepted: 29 September 2025 / Published: 30 September 2025
(This article belongs to the Special Issue Artificial Intelligence for Better Healthcare and Precision Medicine, 2nd Edition)
Versions Notes

Abstract

Older adults often face significant challenges in understanding medical information due to cognitive aging and limited health literacy. Existing simplification models, while effective in general domains, cannot adapt content for elderly users, frequently overlooking narrative tone, readability constraints, and semantic fidelity. In this work, we propose GENSIM—a Generative Expert-Narrated Simplification Model tailored for age-adapted medical text simplification. GENSIM introduces a modular architecture that integrates a Dual-Stream Encoder, which fuses biomedical semantics with elder-friendly linguistic patterns; a Persona-Tuned Narrative Decoder, which controls tone, clarity, and empathy; and a Reinforcement Learning with Human Feedback (RLHF) framework guided by dual discriminators for factual alignment and age-specific readability. Trained on a triad of corpora—SimpleDC, PLABA, and a custom NIH-SeniorHealth corpus—GENSIM achieves state-of-the-art performance on SARI, FKGL, BERTScore, and BLEU across multiple test sets. Ablation studies confirm the individual and synergistic value of each component, while structured human evaluations demonstrate that GENSIM produces outputs rated significantly higher in faithfulness, simplicity, and demographic suitability. This work represents the first unified framework for elderly-centered medical text simplification and marks a paradigm shift toward inclusive, user-aligned generation for health communication.
Keywords: medical text simplification; elderly health literacy; generative language models; persona-aware narrative generation; empathetic health communication; biomedical NLP; inclusive healthcare technology medical text simplification; elderly health literacy; generative language models; persona-aware narrative generation; empathetic health communication; biomedical NLP; inclusive healthcare technology

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MDPI and ACS Style

Abdusalomov, A.; Umirzakova, S.; Mirzakhalilov, S.; Kutlimuratov, A.; Nasimov, R.; Temirov, Z.; Jeong, W.; Choi, H.; Whangbo, T.K. A Generative Expert-Narrated Simplification Model for Enhancing Health Literacy Among the Older Population. Bioengineering 2025, 12, 1066. https://doi.org/10.3390/bioengineering12101066

AMA Style

Abdusalomov A, Umirzakova S, Mirzakhalilov S, Kutlimuratov A, Nasimov R, Temirov Z, Jeong W, Choi H, Whangbo TK. A Generative Expert-Narrated Simplification Model for Enhancing Health Literacy Among the Older Population. Bioengineering. 2025; 12(10):1066. https://doi.org/10.3390/bioengineering12101066

Chicago/Turabian Style

Abdusalomov, Akmalbek, Sabina Umirzakova, Sanjar Mirzakhalilov, Alpamis Kutlimuratov, Rashid Nasimov, Zavqiddin Temirov, Wonjun Jeong, Hyoungsun Choi, and Taeg Keun Whangbo. 2025. "A Generative Expert-Narrated Simplification Model for Enhancing Health Literacy Among the Older Population" Bioengineering 12, no. 10: 1066. https://doi.org/10.3390/bioengineering12101066

APA Style

Abdusalomov, A., Umirzakova, S., Mirzakhalilov, S., Kutlimuratov, A., Nasimov, R., Temirov, Z., Jeong, W., Choi, H., & Whangbo, T. K. (2025). A Generative Expert-Narrated Simplification Model for Enhancing Health Literacy Among the Older Population. Bioengineering, 12(10), 1066. https://doi.org/10.3390/bioengineering12101066

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