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

Year : 2025 | Volume : 12 | Issue : 02 | Page : 31 36
    By

    Devanshi Prajapati,

  • Pranjal Roy Vishwakarma,

  • Bhawana Pillai,

  • Vinod Patel,

  1. Research Scholar, Department of Computer Science and Engineering, Lakshmi Narain College of Technology and 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 and Science (LNCTS), Bhopal, Madhya Pradesh, India
  4. Assistant Professor, Department of Computer Science and Engineering, Lakshmi Narain College of Technology and Science (LNCTS), Bhopal, Madhya Pradesh, India

Abstract

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):31-36.
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):31-36. Available from: https://journals.stmjournals.com/joaira/article=2025/view=208168


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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
256

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