Innovative Applications of Natural Language Processing in Medical Diagnosis Texts in the Era of Large Models

This study explores the innovative applications of Natural Language Processing (NLP) technologies in medical diagnosis texts within the context of large models. With the rapid advancement of deep learning, large-scale models such as GPT-3 and WuDao 2.0, characterized by their massive parameter sizes, have achieved remarkable progress in NLP. This work outlines the critical roles of large models in processing medical diagnosis texts, including text generation, machine translation, summarization, and question-answering systems. Employing state-of-the-art deep learning algorithms and extensive medical diagnosis datasets, we trained and optimized large models to enhance their semantic understanding and reasoning capabilities. Comparative analyses between traditional NLP approaches and large models demonstrate the latter's superiority in improving diagnostic accuracy and efficiency. The findings highlight that large models not only significantly advance the performance of medical text processing but also offer new perspectives and solutions for the medical domain, especially in interpreting complex medical texts and supporting clinical decision-making.