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

Akmalbek Abdusalomov1, Sabina Umirzakova1, Sanjar Mirzakhalilov2,3

  • 1Department of Computer Engineering, Gachon University, Sujeong-Gu, Seongnam-Si 13120, Gyeonggi-Do, Republic of Korea.

Bioengineering (Basel, Switzerland)
|October 29, 2025
PubMed
Summary
This summary is machine-generated.

We developed GENSIM, a new AI model to simplify medical information for older adults. GENSIM enhances understanding by adapting content for age-specific needs, improving health literacy.

Keywords:
biomedical NLPelderly health literacyempathetic health communicationgenerative language modelsinclusive healthcare technologymedical text simplificationpersona-aware narrative generation

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Gerontology

Background:

  • Older adults experience challenges comprehending medical information due to cognitive aging and low health literacy.
  • Current text simplification models fail to address the specific needs of elderly users, neglecting tone, readability, and semantic accuracy.

Purpose of the Study:

  • To introduce GENSIM (Generative Expert-Narrated Simplification Model), a novel framework for age-adapted medical text simplification tailored for older adults.
  • To enhance the accessibility and comprehension of medical information for the geriatric population.

Main Methods:

  • Developed a modular architecture featuring a Dual-Stream Encoder for fusing biomedical semantics and elder-friendly language.
  • Implemented a Persona-Tuned Narrative Decoder to control tone, clarity, and empathy in simplified text.
  • Utilized a Reinforcement Learning with Human Feedback (RLHF) framework with dual discriminators for factual accuracy and age-specific readability.

Main Results:

  • GENSIM achieved state-of-the-art performance across SARI, FKGL, BERTScore, and BLEU metrics on multiple test sets.
  • Ablation studies validated the effectiveness of individual components and their synergistic contributions.
  • Human evaluations confirmed GENSIM outputs are superior in faithfulness, simplicity, and demographic suitability compared to existing methods.

Conclusions:

  • GENSIM represents the first unified framework for elderly-centered medical text simplification.
  • This work signifies a shift towards inclusive, user-aligned AI generation for improved health communication among older adults.