Paper Title
Insights from Medical Documents without the Document

Abstract
This research explores the application of language model-based learning(LLM) techniques to extract valuable insights from vast medical document repositories. Leveraging advanced natural language processing, the study delves into the analysis of electronic health records, research papers and clinical notes to uncover hidden patterns, trends, and potential correlations. By employing LLMs, the study aims to enhance medical decision-making, improve patient outcomes, translation and easy understanding of prescriptions and disease cause, and streamline healthcare processes. The findings highlight the promising potential of LLMs in revolutionizing medical data analysis and opening new avenues for precision medicine and evidence-based healthcare practices.