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AI in Healthcare : Simplifying Medical Reports for Enhanced Patient Comprehension

  • Unique Paper ID: 175571
  • Volume: 11
  • Issue: 11
  • PageNo: 3413-3418
  • Abstract:
  • Medical reports can contain complex and detailed information, making them difficult for patients to understand and healthcare providers to summarize. This project presents a Medical Report Summariser that uses LLAMA models and Retrieval-Augmented Generation (RAG) to generate concise summaries of medical data from two types of inputs: unstructured text (e.g., patient symptoms and issues) and structured documents (e.g., medical reports from clinics or diagnostic centres). The proposed system first extracts relevant medical concepts and terminology from user inputs via text processing and Optical Character Recognition (OCR) for document inputs. Next, the LLAMA model, reinforced by RAG, creates a contextually appropriate summary, obtaining relevant medical knowledge as needed to improve the summarisation. Experimental results show that our method effectively generates coherent and short summaries that help patients understand and increase documentation efficiency for healthcare providers. This paper discusses the methodology, implementation obstacles, and evaluation metrics used to analyse the system's performance before concluding with thoughts on the possible implications of automated medical report summarisation in clinical settings.
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  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 11
  • PageNo: 3413-3418

AI in Healthcare : Simplifying Medical Reports for Enhanced Patient Comprehension

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Journal no 47859

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