Automated Analysis of Medical and MRI Reports Using Large Language Models

Abstract

The abstract presents a concise yet comprehensive overview of the research, emphasizing the growing challenge posed by unstructured medical data in modern healthcare systems. With the increasing adoption of Electronic Health Records (EHRs), healthcare systems generate vast amounts of clinical notes, diagnostic reports, discharge summaries, and laboratory results on a daily basis. Manually reviewing and interpreting this information is time-consuming, cognitively demanding, and susceptible to human error, which can negatively impact clinical workflow and decision-making. The proposed Medical Report Analyzer utilizes the advanced contextual understanding capabilities of Large Language Models (LLMs) to automatically extract clinically relevant information, summarize extensive medical documents, and transform unstructured text into structured, interpretable formats. In addition, the system incorporates process-based learning, enabling it to refine its performance through iterative feedback, contextual validation, and continuous learning from clinical workflows. This approach allows the model to improve accuracy, adaptability, and consistency over time. Designed as a clinical decision-support tool rather than a diagnostic replacement, the system prioritizes patient safety, transparency, and ethical compliance. Experimental evaluation demonstrates notable improvements in efficiency, accuracy, and usability, underscoring the system's potential for scalable and reliable deployment in real-world clinical environments.

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