Summary of Mirage: Multimodal Identification and Recognition Of Annotations in Indian General Prescriptions, by Tavish Mankash et al.
MIRAGE: Multimodal Identification and Recognition of Annotations in Indian General Prescriptions
by Tavish Mankash, V. S. Chaithanya Kota, Anish De, Praveen Prakash, Kshitij Jadhav
First submitted to arxiv on: 13 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel approach to extracting medication names and dosages from handwritten medical records in India. Despite the availability of Electronic Medical Records (EMR), hospitals still rely on handwritten records, which pose a unique challenge for statistical analysis and record retrieval. The authors employ Multimodal Large Language Models (MLLMs) for Optical Character Recognition (OCR) tasks, fine-tuning three pre-trained models on 743,118 high-resolution simulated medical record images annotated by 1,133 Indian doctors. The methodology, MIRAGE, achieves an accuracy of 82% in extracting medication names and dosages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In India, hospitals still use handwritten medical records, making it hard to analyze data and find information. This paper tries to fix this problem using special computer models that can read handwritten text. They took pictures of simulated medical records written by Indian doctors and taught the models to recognize medicines and their correct dosages. The team’s approach, called MIRAGE, was able to get 82% accurate in identifying medications. |
Keywords
» Artificial intelligence » Fine tuning
