Home / Current Issue / Paper 1724204
AI-Based Intelligent Medical Report Understanding and Personal Health Management System
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence and Machine Learning
DOI: 10.64388/IREV10I4-1724204
Abstract
Medical reports contain important health information, but their technical terminology and numerical results may be difficult for patients to understand. Recent studies have explored the use of large language models to simplify radiology reports, while also identifying concerns related to accuracy, omitted information, and potentially misleading explanations [1–3]. This paper proposes an AI-Based Intelligent Medical Report Understanding and Personal Health Management System designed to make medical information easier to understand and organize. The proposed framework will accept medical reports in PDF or image format, extract relevant text, and generate patient-friendly explanations while aiming to preserve important values, units, and reference ranges. A retrieval-based component is proposed to support explanations using reliable medical sources. Additional planned features include date-wise health-record organization, comparison of comparable report values, and reminders based on user-entered or clinician-provided instructions. The framework emphasizes privacy, factual consistency, and the distinction between educational information and professional medical advice. Its evaluation will examine text-extraction accuracy, preservation of medical information, factual consistency, readability, and usability after implementation. As the system has not yet been experimentally evaluated, this paper presents the proposed design and evaluation plan rather than measured performance results.
Keywords
artificial intelligence; health information management; large language models; medical report simplification; patient education
References
[1] Doshi R, Amin KS, Khosla P, Bajaj SS, Chheang S, Forman HP. Quantitative evaluation of large language models to streamline radiology report impressions: a multimodal retrospective analysis. Radiology. 2024;310(3):e231593. https://doi.org/10.1148/radiol.231593
[2] Jeblick K, Schachtner B, Dexl J, Mittermeier A, Stüber AT, Topalis J, et al. ChatGPT makes medicine easy to swallow: an exploratory case study on simplified radiology reports. Eur Radiol. 2024;34(5):2817–25. https://doi.org/10.1007/s00330-023-10213-1
[3] Alabed S, et al. Large language models for simplifying radiology reports: a systematic review and meta-analysis of patient, public, and clinician evaluations. Lancet Digit Health. 2026;8(2):100960. https://doi.org/10.1016/j.landig.2025.100960
How to cite this paper
@article{1724204,
author = {Aksa Sarfraj Shaikh},
title = {AI-Based Intelligent Medical Report Understanding and Personal Health Management System},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {4},
pages = {3186-3190},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1724204.pdf},
abstract = {Medical reports contain important health information, but their technical terminology and numerical results may be difficult for patients to understand. Recent studies have explored the use of large language models to simplify radiology reports, while also identifying concerns related to accuracy, omitted information, and potentially misleading explanations [1–3]. This paper proposes an AI-Based Intelligent Medical Report Understanding and Personal Health Management System designed to make medical information easier to understand and organize. The proposed framework will accept medical reports in PDF or image format, extract relevant text, and generate patient-friendly explanations while aiming to preserve important values, units, and reference ranges. A retrieval-based component is proposed to support explanations using reliable medical sources. Additional planned features include date-wise health-record organization, comparison of comparable report values, and reminders based on user-entered or clinician-provided instructions. The framework emphasizes privacy, factual consistency, and the distinction between educational information and professional medical advice. Its evaluation will examine text-extraction accuracy, preservation of medical information, factual consistency, readability, and usability after implementation. As the system has not yet been experimentally evaluated, this paper presents the proposed design and evaluation plan rather than measured performance results.},
keywords = {artificial intelligence; health information management; large language models; medical report simplification; patient education},
month = {October},
doi = {https://doi.org/10.64388/IREV10I4-1724204}
}