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.

翻译：胃肠道癌症在全球癌症负担中占据相当大比例，其早期诊断对于改善疾病管理和患者预后至关重要。胃肠道癌症的复杂病因和重叠症状常导致诊断延迟，从而造成治疗策略欠佳。癌症相关查询对于及时诊断、治疗和患者教育具有关键作用，因为获取准确全面的信息能显著影响治疗结果。然而，癌症疾病的复杂性结合海量可用数据，使得临床医生和患者难以快速获取精确答案。为应对这些挑战，我们利用GPT-3.5 Turbo等大型语言模型生成针对癌症查询的准确且符合语境的回答。这些经过医学数据预训练的模型能提供及时、可操作的见解，支持癌症诊断与护理中的知情决策，最终改善患者预后。我们计算了两个评估指标：A1（表示模型生成答案中存在的实体占黄金标准答案的比例）和A2（表示模型生成答案相对于黄金标准答案的语言正确性与意义完整性），分别达到0.546和0.881的最高值。