Abstract

Rapidly expanding medical text data (clinical notes, EHRs, biological literature) requires efficient summarization.   Manual interpretation of such big data is unfeasible and time-consuming in time-sensitive healthcare settings. To solve this problem, we offer a medical text summarisation system using BART (Bidirectional and Auto-Regressive Transformer), PEFT, and Low-Rank Adaptation. Lower-parameter big language model refinement is computationally efficient using this strategy.   Our method produces high-quality, coherent summaries in resource-limited situations.   Our trials optimized 73,728 parameters out of 406,364,160, or 0.0181%, simplifying model training. Even with poor conditioning, the algorithm produced contextually sensitive summaries with medical content. Without modifying the architecture, LoRA enables task-specific learning via low-rank matrix decomposition and efficient task adaptability. This includes healthcare diagnostic and clinical trial reports. Our method outperformed complete fine-tuning in training time, memory usage, and scalability on benchmark medical datasets.   These outcomes also demonstrate that our system is promising for realistic institutional changes. This study shows how resource-efficient, scalable medical summarization systems may work. Our solution reduces the computing load of related methods, enabling AI-powered healthcare applications. This concept enhances fine-tuning and helps construct intelligent systems to process and summarise complex medical data. The proposed approach supports parameter-efficient adaptation research, especially in key application domains where accuracy and efficiency are crucial.

Associate editor: Hinda Gmati