Clinic Electronic Medical Record Image Dataset

In the healthcare industry, the use of electronic medical records is increasing, but related Q&A systems still face significant challenges, such as the difficulty of parsing medical texts in various formats and insufficient Q&A accuracy. Existing solutions cannot fully utilize image information and lack in-depth exploration of image data, making it difficult to accurately answer medical-related questions. This dataset aims to solve the issues of electronic medical record image parsing and automatic question generation to meet business needs of healthcare providers to improve efficiency and accuracy.The data is collected using high-resolution scanning equipment in a standardized clinic environment. Data quality is ensured through multiple rounds of annotation, consistency checks, and expert reviews, with the annotation team composed of experts with a medical background. In data preprocessing, OCR technology is employed to convert medical records into an analyzable text format, and image enhancement is utilized to improve recognition rates. Data is stored in JPG format and organized hierarchically by patient, medical history, etc.This dataset features extremely high image annotation accuracy and consistency, with completeness exceeding 98%. Innovations include automatic Q&A generation technology for medical record images and a unique multimodal data fusion method, improving information retrieval accuracy by 15%. This dataset not only addresses practical issues of medical record parsing but also enhances the reliability of intelligent diagnostic systems. Compared to other datasets, it provides greater scalability and versatility by introducing more detailed image data, making it suitable for medical institutions of various sizes. After integration into medical record Q&A systems, the Q&A accuracy increased by an average of 20% and offers rarity for special medical cases.