Enhanced Biomedical Why Question Answering Using Transfer Learning And Federated learning Approach

Recent advancements in natural language processing (NLP) have led to the creation of advanced language models for various applications. However, large language models (LLMs) often underperform in biomedical question answering (QA) due to their limitations in processing specialized medical vocabulary and meeting the high accuracy standards demanded in the field. To address these challenges, we present WhyMedQA, a biomedical question-answering system featuring a specialized transformer-based model designed for the biomedical domain. This model is based on the BART architecture, incorporating domain-specific modifications and additional layers to enhance its understanding of complex biomedical concepts and generate precise, contextually relevant responses. By fine-tuning on specialized datasets like BioASQ8 and the expert-annotated subset of PubMedQA, our model effectively recognizes the intricate structure of biomedical language. Experimental results demonstrate that our proposed model outperforms baseline models by achieving reduced training and validation losses and higher BLEU and ROUGE scores. Importantly, it accomplishes this with significantly fewer parameters, making it suitable for deployment in resource-constrained environments. This study contributes to medical NLP by integrating domain-specific model improvements, potentially reducing medical decision-making errors and improving patient outcomes globally.