Cancer-Answer: Empowering Cancer Care with Advanced Large Language Models
This work addresses the problem of delayed diagnosis and suboptimal treatment in cancer care by offering a tool for clinicians and patients, but it is incremental as it applies existing LLMs to a specific medical domain.
The paper tackled the challenge of providing accurate and timely answers to cancer-related queries, particularly for gastrointestinal tract cancers, by leveraging large language models like GPT-3.5 Turbo, achieving maximum entity coverage of 0.546 and linguistic correctness of 0.881.
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.