Cancer-Answer: Empowering Cancer Care with Advanced Large Language Models

TL;DR

This paper explores the use of advanced large language models, like GPT-3.5 Turbo, to generate accurate, contextually relevant answers for cancer-related queries, aiming to improve early diagnosis and patient care in gastrointestinal cancers.

Contribution

It demonstrates how pre-trained large language models can be adapted to provide reliable, timely information for cancer diagnosis and management, enhancing clinical decision-making.

Findings

Achieved A1 score of 0.546 indicating entity coverage

Achieved A2 score of 0.881 indicating answer quality

Showed potential for LLMs to support cancer care decisions

Abstract

Gastrointestinal (GI) tract cancers account for a substantial portion of the global cancer burden, where early diagnosis is critical for improved management and patient outcomes. The complex aetiologies and overlapping symptoms across GI cancers often delay diagnosis, leading to suboptimal treatment strategies. Cancer-related queries are crucial for timely diagnosis, treatment, and patient education, as access to accurate, comprehensive information can significantly influence outcomes. However, the complexity of cancer as a disease, combined with the vast amount of available data, makes it difficult for clinicians and patients to quickly find precise answers. To address these challenges, we leverage large language models (LLMs) such as GPT-3.5 Turbo to generate accurate, contextually relevant responses to cancer-related queries. Pre-trained with medical data, these models provide timely, actionable insights that support informed decision-making in cancer diagnosis and care, ultimately improving patient outcomes. We calculate two metrics: A1 (which represents the fraction of entities present in the model-generated answer compared to the gold standard) and A2 (which represents the linguistic correctness and meaningfulness of the model-generated answer with respect to the gold standard), achieving maximum values of 0.546 and 0.881, respectively.