Building an AI-based Model to Extract and Classify Contents from Analog Medical History Forms

Medical history forms, often lacking standardization, pose challenges for healthcare professionals. This study employs an AI-based model to digitize and categorize these forms, enhancing accessibility. Results show the model achieved 79.65% accuracy with nine documents, rising to 88.74% with 99 documents, demonstrating the potential for improved medical history documentation.Clinical Relevance: This model serves as a tool to enhance universal medical text classification for quick understanding, reduce costs, save time, and minimize errors in patient history-taking. It can save doctors time and enhance clinical efficiency.