Machine Learning-Augmented AI System for Medical Report Analysis, Clinical Summarization, and Diagnostic Decision Support

Abstract

The extensive adoption of electronic health records has necessitated the development of automated systems that are capable of understanding unstructured clinical documents. Medical records, such as lab results, radiology findings, and discharge summaries, thus make manual analysis a slow and error-prone process. The paper introduces an AI-driven medical report analysis framework that employs natural language processing and deep learning to automatically locate and interpret the clinically significant information. The system proposed in this paper first preprocesses the medical text to identify the major entities such as diseases, symptoms, and drugs, and then translates them into structured clinical data. An attention-based neural model is used to produce brief analytical summaries, which help clinical decision-making. Experimentally, it was found that the proposed system not only outperformed the manual process in accuracy but also reduced the time. The framework, therefore, increases the efficiency of healthcare and opens up the potential for better utilization of electronic medical records.