Augmenting AI with Context: Hybrid Generative Models for Summarizing Complex Medical Texts

The rapid advancements in the area of generative AI have opened up new fields for the summarization of texts. However, medical texts are complex, technical, and usually require much more contextual accuracy while summarizing them. The authors present a hybrid generative framework that integrates pre-trained language models with external medical knowledge resources to improve the accuracy, coherence, and contextual relevance of medical text summarization. This model will address the weaknesses of traditional generative approaches, as it utilizes external sources of data like clinical guidelines, ontologies, and patient-specific records. The performance of the framework was measured with ROUGE, BLEU, BERTScore, and validation from experts on its performance in medical summarization tasks compared to the conventional models. The results indicate the critical role of context-aware systems in the healthcare domain and the potential of hybrid methods to enhance both the quality of summarization and their practical applicability.