AI Health Care Report Explainer

Abstract

This project presents an AI-based system for automated analysis of medical reports. Users can upload reports in PDF or image format, and Optical Character Recognition (OCR) is used to extract text data. The extracted information is analyzed using AI to generate a concise summary, identify key medical parameters, and compare them with standard values. The system classifies health status into risk levels such as normal, moderate, or high, and provides basic health suggestions along with doctor recommendations. All reports and results are stored in a database and can be managed through an admin dashboard. This solution reduces manual effort, improves accuracy, and provides quick and accessible medical insights for both patients and healthcare professionals.

Introduction

The text presents an AI-based Medical Report Analysis System designed to help patients understand complex medical reports without needing medical expertise. It addresses the difficulty and time consumption involved in manually interpreting reports and aims to improve accessibility, accuracy, and ease of understanding in healthcare.

The proposed system allows users to upload medical reports in PDF or image formats, which are processed using Optical Character Recognition (OCR) to extract text. The extracted data is then analyzed using AI and NLP techniques to identify key medical parameters and compare them with standard reference values. Based on this analysis, the system classifies health status into risk levels such as normal, moderate, or high and generates structured outputs including summaries, parameter-wise comparisons, and basic health recommendations. It also stores processed data in a database and provides an admin dashboard for monitoring.

The literature survey highlights existing work in AI-driven healthcare systems, retrieval-augmented generation (RAG), explainable AI, and healthcare management platforms. While these studies demonstrate progress in predictive healthcare and AI-based analysis, they often lack patient-friendly report interpretation, real-time integration, standardized evaluation, or practical deployment focused on simplifying medical reports.

The proposed system is implemented as a MERN stack web application. After report upload, OCR extracts text which is processed using AI/NLP to evaluate medical parameters against normal ranges. The system outputs structured tables showing parameter values, status, and risk level, along with summaries and health suggestions. It also provides recommendations for nearby specialist doctors based on results.

The workflow includes user login, report upload, secure storage, OCR-based text extraction, and AI-driven analysis leading to structured medical insights. Overall, the system aims to reduce manual effort and help patients quickly and accurately understand their medical reports while supporting healthcare professionals with efficient digital analysis tools.

Conclusion

1) The AI Healthcare Report Explainer successfully addresses the challenge of understanding complex medical reports by providing an automated and intelligent analysis system. It enables users to upload medical reports, extract relevant data using OCR, and analyze it through AI techniques to generate clear and structured results. 2) The system effectively presents key medical parameters, compares them with standard values, and classifies health conditions into different risk levels. It also provides simplified summaries, basic health suggestions, and doctor recommendations, making medical information more accessible to non-expert users. 3) By reducing manual effort and improving accuracy, the system enhances the efficiency of healthcare report interpretation for both patients and administrators. Although the system depends on OCR quality and AI model accuracy, it demonstrates strong potential for future improvements and real-world healthcare applications.

References

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Copyright

Copyright © 2026 K. Manoj, D. Deepika, S. Pree Sowmya, K. Sandeep. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.