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This system analyzes medical reports to enhance understanding among diverse linguistic communities, suggesting effective healthcare communication strategies.
Healthcare accessibility remains a significant challenge in developing nations where patients struggle to comprehend complex medical terminology in diagnostic reports. This research presents an intelligent webbased system bridging the gap between medical jargon and patient understanding through artificial intelligence. The system accepts medical reports in text and PDF formats, utilizing Optical Character Recognition to extract information from scanned documents. Natural Language Processing algorithms analyze extracted content to identify medical conditions, generate simplified summaries, and recommend precautions. The system provides multilanguage support for twelve Indian regional languages, making healthcare information accessible to diverse linguistic communities. Built on React and TypeScript, the frontend delivers responsive performance while the Hugging Face-hosted backend handles OCR processing, medical text analysis, and language translation. Testing across fifty sample reports demonstrated ninety percent overall accuracy, with OCR achieving ninety-four percent accuracy on clear documents. User satisfaction averaged four point five out of five. The system maintains strict privacy by processing data in real-time without permanent storage.
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Parjane et al. (2026) studied this question.
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