用大模型提升胃肠道癌问诊效率，回答更准更贴切。
Cancer-Answer: Empowering Cancer Care with Advanced Large Language Models
- 基于GPT-3.5 Turbo构建癌症问答系统，结合医学预训练
- 实体匹配率最高0.546，语言正确性达0.881
- 适合临床医生和患者快速获取精准癌症信息
胃肠道（GI）癌占全球癌症负担的很大比例，早期诊断对改善管理及患者预后至关重要。由于病因复杂且症状重叠，常导致诊断延迟，进而影响治疗效果。癌症相关问询在及时诊断、治疗与患者教育中起关键作用，准确全面的信息可显著改善结局。然而，癌症本身复杂且数据量庞大，使医患难以快速获取精确答案。为此，我们采用GPT-3.5 Turbo等大语言模型（LLMs），基于医学数据预训练，生成针对癌症问题的准确、上下文相关的回答，为诊断与治疗提供及时可行的决策支持，从而改善患者结果。我们计算两个指标：A1（模型回答中实体与标准答案的匹配比例），最高达0.546；A2（模型回答的语言正确性与意义完整性），最高达0.881。
原文摘要 · Abstract (English)
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.
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