Premium Unlocked AI for Medical Document Decoding

Title

Premium Unlocked AI for Medical Document Decoding

Creator

Sherwin Prince Daniel, R.; Bijeesh, T.V.; Shamanth, N.

Description

As healthcare systems evolve to become more digital, an enormous volume of medical data is available in various formats, including unstructured data, scanned documents, handwritten prescriptions, diagnostic images, audio transcriptions, and clinical video recordings. The complexity and unorganised form of data continue to pose serious challenges with regard to automation, accuracy, and consistency in healthcare and insurance businesses. This study introduces an AI-based multimodal framework that incorporates the use of Optical Character Recognition (OCR), the MiniCPMV-4.5 model, and Retrieval-Augmented Generation (RAG) with Large Language Models (LLMs) to enhance the intelligent processing and contextual comprehension of intricate medical data, thus overcoming these limitations. It applies OCR to scanned images and handwritten documents to precisely recover the textual information from them and uses domain-specific named entity recognition (NER) to recognize significant medical information, e.g., patient information, diagnoses, procedures, and financial information. The extracted information is then converted to vector embeddings and stored in a powerful vector database, Milvus, that enables fast and efficient semantic search as well as context-sensitive reasoning. The proposed framework, along with the visual and auditory inputs, video understanding, multilingual capacity, and the S2S (speech-to-speech) and TTS (text-to-speech) translation, makes it more accessible and engaging to the user. This system reduces the level of human involvement and provides real-time insights quickly and more precisely so that more efficient decisions and operations can be made in the fields of healthcare and insurance. 2025 IEEE.

Source

International Conference on NexGen Networks and Cybernetics, IC2NC 2025 - Proceedings;pp.774-780

Date

01-01-2025

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Artificial Intelligence; Deep Learning; Large Language Models (LLMs); Medical Document Analysis; Multilingual Processing; Named Entity Recognition (NER); Optical Character Recognition (OCR); Retrieval Augmented Generation (RAG); Speech-to-Text (STT); Text-to-Speech (TTS)

Coverage

Sherwin Prince Daniel R., Christ University, Department of AI, Ml & Data Science, Bangalore, India; Bijeesh T.V., Christ University, Department of AI, Ml & Data Science, Bangalore, India; Shamanth N., Christ University, Department of Computer Science & Engineering, Bangalore, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 979-833159484-8;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/IC2NC67409.2025.11376438

https://www.scopus.com/pages/publications/105035605413?origin=resultslist