Applications of Large Language Models in Pathology

Abstract

Large language models (LLMs) are transformer-based neural networks that can provide human-like responses to questions and instructions. LLMs can generate educational material, summarize text, extract structured data from free text, create reports, write programs, and potentially assist in case sign-out. LLMs combined with vision models can assist in interpreting histopathology images. LLMs have immense potential in transforming pathology practice and education, but these models are not infallible, so any artificial intelligence generated content must be verified with reputable sources. Caution must be exercised on how these models are integrated into clinical practice, as these models can produce hallucinations and incorrect results, and an over-reliance on artificial intelligence may lead to de-skilling and automation bias. This review paper provides a brief history of LLMs and highlights several use cases for LLMs in the field of pathology.

Floating-Point Embedding: Enhancing the Mathematical Comprehension of Large Language Models

“…This breakthrough has given rise to a series of pre-trained language models, including GPT [2] and BERT [3], which have demonstrated remarkable achievements in natural language processing. These models, trained on extensive unlabeled corpora, acquire knowledge of language rules, extract common-sense information embedded in text, and attain a generalized language representation, significantly elevating their performance across various downstream tasks [4][5][6].…”