Bridging the Gap and Unlocking Health Literacy: A Guide to Medical Report Clarity

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2025 | Volume : 12 | Issue : 02 | Page : –
    By

    Devanshi Prajapati,

  • Pranjal Roy Vishwakarma,

  • Bhawana Pillai,

  • Vinod Patel,

  1. Research Scholar, Department of Computer Science and Engineering, Lakshmi Narain College of Technology & Science (LNCTS), Bhopal, Madhya Pradesh, India
  2. Data Engineer, Department of Computer Science, Pune, Maharashtra, India
  3. Professor, Department of Computer Science and Engineering, Lakshmi Narain College of Technology & Science (LNCTS), Bhopal, Madhya Pradesh, India
  4. Assistant Professor, Department of Computer Science and Engineering, Lakshmi Narain College of Technology & Science (LNCTS), Bhopal, Madhya Pradesh, India

Abstract

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This research highlights the significant challenges faced by the general population in interpreting laboratory reports, medical charts, and other health-related documents. Studies show that approximately 9 out of 10 individuals struggle to understand such medical information, primarily due to low health literacy levels. This lack of understanding has become a hidden epidemic, affecting the way people engage with and respond to their own healthcare. While medical tests are essential for diagnosing illnesses, tracking disease progression, and evaluating treatment efficacy, their interpretation typically requires expertise in the medical field. This raises an important concern: how can the average person make sense of complex medical data? To address this issue, the proposed research introduces a framework that leverages advanced technologies such as transformer models, sequence-to-sequence (Seq2Seq) learning, natural language processing (NLP), text summarization, and model fine-tuning. The goal is to simplify complex health data, enabling individuals to interpret their medical reports more clearly and confidently, thereby promoting better health decisions and
outcomes.

Keywords: Transformer models, Seq2seq, text summarization, natural language processing and finetuning

[This article belongs to Journal of Artificial Intelligence Research & Advances ]

How to cite this article:
Devanshi Prajapati, Pranjal Roy Vishwakarma, Bhawana Pillai, Vinod Patel. Bridging the Gap and Unlocking Health Literacy: A Guide to Medical Report Clarity. Journal of Artificial Intelligence Research & Advances. 2025; 12(02):-.
How to cite this URL:
Devanshi Prajapati, Pranjal Roy Vishwakarma, Bhawana Pillai, Vinod Patel. Bridging the Gap and Unlocking Health Literacy: A Guide to Medical Report Clarity. Journal of Artificial Intelligence Research & Advances. 2025; 12(02):-. Available from: https://journals.stmjournals.com/joaira/article=2025/view=0



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References

  1. Palex Group» Why Medical Terminology is Tough, and How to Tackle It?. Palexgroup.com. 2022. Available from: https://palexgroup.com/blog/medical-terminology-translation/
  2. Jain R, Jangra A, Saha S, Jatowt A. A survey on medical document summarization. arXiv preprint arXiv:2212.01669. 2022 Dec 3.
  3. Zheng G, Wang X, Liang J, Chen N, Zheng Y, Wang B. Efficiently Democratizing Medical LLMs for 50 Languages via a Mixture of Language Family Experts. arXiv preprint arXiv:2410.10626. 2024 Oct 14.
  4. Li X, Lan L, Lahza H, Yang S, Wang S, Yang W, Liu H, Zhang Y. EAFP-Med: An efficient adaptive feature processing module based on prompts for medical image detection. Expert Systems with Applications. 2024 Aug 1;247:123334.
  5. The Devastator. Medical Conversation Corpus (100k+). Kaggle.com. 2023. Available from: https://www.kaggle.com/datasets/thedevastator/medical-conversation-corpus-100k
  6. Gero Z, Singh C, Xie Y, Zhang S, Subramanian P, Vozila P, Naumann T, Gao J, Poon H. Attribute Structuring Improves LLM-Based Evaluation of Clinical Text Summaries. arXiv preprint arXiv:2403.01002. 2024 Mar 1.
  7. Shamshad F, Khan S, Zamir SW, Khan MH, Hayat M, Khan FS, Fu H. Transformers in medical imaging: A survey. Medical image analysis. 2023 Aug 1;88:102802.
  8. Sun D, He J, Zhang H, Qi Z, Zheng H, Wang X. A LongFormer-Based Framework for Accurate and Efficient Medical Text Summarization. arXiv preprint arXiv:2503.06888. 2025 Mar 10.
  9. Helwan A, Azar D, Ozsahin DU. Medical reports summarization using text-to-text transformer. In2023 Advances in Science and Engineering Technology International Conferences (ASET) 2023 Feb 20 (pp. 01-04). IEEE.
  10. Agilandeeswari L, Dagar A, Deepthi A, Arangasakthivel R. Automatic Text Summarization for Medical Dataset-An Analysis. InInternational Conference on Intelligent Systems Design and Applications 2023 Dec 11 (pp. 336-352). Cham: Springer Nature Switzerland.
Regular Issue Subscription Original Research
Volume 12
Issue 02
Received 04/02/2025
Accepted 30/03/2025
Published 16/04/2025
Publication Time 71 Days
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