AI-Driven Medical Report Summarization and Intelligent Abnormality Detection System
DOI:
https://doi.org/10.15680/IJCTECE.2026.0903006Keywords:
Artificial Intelligence, Natural Language Processing, Medical Report Analysis, Abnormality Detection, Machine Learning, Healthcare AnalyticsAbstract
Artificial Intelligence (AI) has significantly transformed the healthcare sector by enabling efficient analysis of large-scale medical data. This paper presents an AI-driven medical report summarization and intelligent abnormality detection system designed to automate the interpretation of clinical reports and enhance healthcare decision-making. The proposed system integrates Natural Language Processing (NLP) and machine learning techniques to process both structured and unstructured medical data. The system extracts key clinical entities such as test parameters, values, and diagnostic indicators using advanced NLP techniques including tokenization, normalization, and named entity recognition. Extracted data is then analyzed using machine learning models to detect abnormalities by comparing values with standard medical reference ranges. In addition, the system generates concise and meaningful summaries from lengthy medical reports, improving readability and accessibility for both patients and healthcare professionals. The proposed model improves efficiency, reduces manual effort, and enhances the accuracy of medical report interpretation. It provides a scalable and adaptable solution suitable for real-world healthcare applications.
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