MediScanML: Intelligent Prescription Recognition
Keywords:
Deep learning, Healthcare automation, Machine Learning (ML), Medical prescription recognition, Natural Language Processing (NLP), Optical Character Recognition (OCR)Abstract
Medical prescription recognition is a crucial task in healthcare, aiming to automate the extraction of medication details from handwritten or printed prescriptions using Machine Learning (ML) techniques. Traditional methods, including Optical Character Recognition (OCR), struggle with handwritten prescriptions due to variations in handwriting styles, abbreviations, and unclear formatting. This study explores an ML-based approach that integrates computer vision and Natural Language Processing (NLP) to accurately recognize, extract, and classify text from medical prescriptions. A dataset comprising diverse handwritten and printed prescriptions is used for training and testing deep learning models, including Convolutional Neural Networks (CNNs) for image processing and Transformer-based NLP models for text interpretation. The methodology involves preprocessing techniques such as noise removal, segmentation, and feature extraction to enhance recognition accuracy. Experimental results demonstrate that our model achieves high accuracy in detecting drug names, dosages, and instructions, outperforming traditional OCR-based methods. The study highlights key challenges such as variations in handwriting, ambiguous medical abbreviations, and the need for large, diverse datasets to improve generalization. The proposed ML-based system has the potential to enhance efficiency in pharmacies and healthcare systems by reducing human errors, improving prescription processing speed, and integrating with Electronic Health Records (EHRs).
Furthermore, our findings suggest that combining vision and NLP-based approaches significantly improves recognition performance. Future research will focus on enhancing model robustness, expanding dataset diversity, and integrating the system with real-world healthcare infrastructures. Additionally, the use of multimodal deep learning techniques could further refine prescription recognition accuracy, enabling seamless automation in medical record management. Ethical concerns, including data privacy and security, must also be addressed before large-scale deployment. This research underscores the transformative potential of ML in automating medical prescription processing, paving the way for improved patient safety, reduced workload for healthcare professionals, and enhanced efficiency in the pharmaceutical industry.
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