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In today’s healthcare systems, managing the vast and growing volume of clinical text, particularly pathological reports, remains a pressing challenge. To address this, we introduce an automated summarization framework designed to distill essential information from lengthy medical documents. The proposed system combines a Transformer-based encoder-decoder architecture with a Generative Adversarial Network (GAN) to enhance the accuracy and fluency of generated summaries. Prior to modeling, the input text undergoes rule-based preprocessing and Named Entity Recognition (NER) to identify and retain critical medical terms while eliminating irrelevant data. The Transformer module effectively captures complex contextual relationships within the document, while the GAN discriminator improves the summary’s coherence through adversarial refinement. We evaluated our model on a clinical dataset using standard summarization metrics, including ROUGE-1, ROUGE-2, and ROUGE-L. Comparative analysis with existing models such as BERTSUM and TextRank indicates that our approach yields more relevant and concise summaries. This solution aims to support healthcare professionals by streamlining the review of clinical texts and facilitating faster decision-making.

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Automating Medical Report Summarization: A Generative AI Approach for Enhanced Decision Support and Workflow Efficiency in Healthcare

  • 会议论文

  • 发表日期：

    2026年

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  • Palak Hajare,
  • Mallika Hariharan,
  • Snehal V. Laddha

摘要

In today’s healthcare systems, managing the vast and growing volume of clinical text, particularly pathological reports, remains a pressing challenge. To address this, we introduce an automated summarization framework designed to distill essential information from lengthy medical documents. The proposed system combines a Transformer-based encoder-decoder architecture with a Generative Adversarial Network (GAN) to enhance the accuracy and fluency of generated summaries. Prior to modeling, the input text undergoes rule-based preprocessing and Named Entity Recognition (NER) to identify and retain critical medical terms while eliminating irrelevant data. The Transformer module effectively captures complex contextual relationships within the document, while the GAN discriminator improves the summary’s coherence through adversarial refinement. We evaluated our model on a clinical dataset using standard summarization metrics, including ROUGE-1, ROUGE-2, and ROUGE-L. Comparative analysis with existing models such as BERTSUM and TextRank indicates that our approach yields more relevant and concise summaries. This solution aims to support healthcare professionals by streamlining the review of clinical texts and facilitating faster decision-making.