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International Research Journal of Engineering and Technology (IRJET) Volume: 13 Issue: 02 | Feb 2026

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e-ISSN: 2395-0056 p-ISSN: 2395-0072

AI-POWERED MEDICAL LAB REPORT ANALYSIS & HEALTH INSIGHTS PLATFORM Mrs. M. Sushma1 A. Roshini2, C. Sai Krishna Reddy3, D. Akshith4, G. Bharath Kumar Reddy5 1Asst.Professor in Department of IT, TKR College of Engineering and Technology, Telangana, India

2345BTECH Students in Department of IT, TKR College of Engineering and Technology, Telangana, India

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Abstract - Medical laboratory reports are essential for

images that include complex medical terminology and tabular structures. As a result, most patients are unable to interpret their health conditions accurately without professional medical assistance.

clinical diagnosis, treatment planning, and long-term health monitoring. Despite the widespread adoption of electronic health record systems, a large number of laboratory reports are still generated and shared in paper-based or PDF formats, making automated processing and interpretation difficult. These reports contain complex medical terminology, numerical values, and reference ranges that are not easily understandable by non-medical users.

From a healthcare provider’s perspective, handling large volumes of lab reports involves manual reading, data entry, and interpretation, which is both time-consuming and errorprone. Existing digital systems mainly focus on storing reports rather than analyzing or explaining them. This creates a gap between raw medical data and meaningful health insights.

This paper presents an AI-powered medical laboratory report analysis and health insights platform that automatically extracts, interprets, and visualizes laboratory test information from PDF and scanned reports. The proposed system employs Optical Character Recognition (OCR) to extract textual content from unstructured documents and Named Entity Recognition (NER) techniques to identify key medical entities such as test names, result values, units, and reference ranges. An intelligent interpretation module compares extracted values with standard medical thresholds to classify results as Low, Normal, or High and generates patient-friendly health insights. The system also provides visual dashboards to help users understand test results and monitor trends over time.

To overcome these challenges, Artificial Intelligence (AI), Natural Language Processing (NLP), and Optical Character Recognition (OCR) technologies can be effectively used to automate lab report analysis. AI-driven systems are capable of extracting relevant medical information, interpreting results, and presenting simplified insights in a user-friendly manner. 1.1 AI-Based Medical Lab Report Analysis System The AI-based medical lab report analysis system is designed to automatically read, understand, and interpret laboratory reports using advanced machine learning and NLP techniques. The system accepts lab reports in PDF or image formats and applies OCR to extract textual data from the documents. This extracted text is then processed using NLP models to identify important medical entities such as test names, result values, units, and reference ranges.

Experimental evaluation demonstrates that the proposed system achieves high extraction accuracy, reduces manual data entry, and significantly improves patient understanding of laboratory reports. Unlike traditional extraction-only approaches, the proposed platform integrates automated interpretation and visualization, making it a scalable and user-centric solution for modern digital healthcare systems.

Once the medical entities are identified, the system compares the extracted values with standard biological reference ranges to determine whether the results are Low, Normal, or High. Based on this classification, the system generates simplified explanations and basic health insights that are easy for non-medical users to understand. This automated approach significantly reduces manual effort and improves the accessibility of medical information.

The proposed system emphasizes end-user understanding by combining automated extraction with intelligent interpretation and visualization. Keywords: Optical Character Recognition, Medical Document Processing, PDF Extraction, Named Entity Recognition, Healthcare Artificial Intelligence 1. INTRODUCTION

1.2 Models and Technologies Used for Lab Report Interpretation

In today’s healthcare ecosystem, laboratory test reports are one of the most important tools used for medical diagnosis, treatment planning, and long-term health monitoring. These reports contain critical clinical information such as test names, numerical values, reference ranges, and measurement units. However, lab reports are usually delivered to patients in the form of PDF files or scanned

Impact Factor value: 8.315

The proposed system utilizes multiple AI and NLP models to ensure accurate and reliable lab report analysis. Optical Character Recognition (OCR) models such as PP-OCR or cloud-based OCR services are used to convert scanned documents into machine-readable text. These models are

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