Multidisciplinary tumour boards (MDTBs) play a central role in oncology decision-making but require manual processes and structuring large volumes of heterogeneous clinical information, resulting in a substantial documentation burden. In this work, we present ONCOTIMIA, a modular and secure clinical tool designed to integrate generative artificial intelligence (GenAI) into oncology workflows and evaluate its application to the automatic completion of lung cancer tumour board forms using large language models (LLMs). The system combines a multi-layer data lake, hybrid relational and vector storage, retrieval-augmented generation (RAG) and a rule-driven adaptive form model to transform unstructured clinical documentation into structured and standardised tumour board records. We assess the performance of six LLMs deployed through AWS Bedrock on ten lung cancer cases, measuring both completion form accuracy and end-to-end latency. The results demonstrate high performance across models, with the best performing configuration achieving an 80% of correct field completion and clinically acceptable response time for most LLMs. Larger and more recent models exhibit best accuracies without incurring prohibitive latency. These findings provide empirical evidence that LLM- assisted autocompletion form is technically feasible and operationally viable in multidisciplinary lung cancer workflows and support its potential to significantly reduce documentation burden while preserving data quality.

翻译：多学科肿瘤委员会（MDTBs）在肿瘤学决策中发挥着核心作用，但其处理过程依赖人工操作，且需整合大量异构临床信息，导致文档负担沉重。本研究提出ONCOTIMIA——一个模块化且安全的临床工具，旨在将生成式人工智能（GenAI）整合到肿瘤学工作流程中，并通过大型语言模型（LLMs）评估其在肺癌肿瘤委员会表单自动填写中的应用。该系统结合多层数据湖、混合关系与向量存储、检索增强生成（RAG）以及规则驱动的自适应表单模型，将非结构化临床文档转化为结构化、标准化的肿瘤委员会记录。我们通过AWS Bedrock部署的六种LLM对十例肺癌病例进行性能评估，同时测量表单填写准确性和端到端延迟。结果显示各模型均表现优异，最优配置实现了80%的正确字段填充率，且多数LLM的响应时间达到临床可接受水平。更大规模及更新版本的模型展现出最佳准确性，且未产生过高延迟。这些发现为LLM辅助的自动填表技术在多学科肺癌工作流程中的技术可行性与操作适用性提供了实证依据，证实其在保障数据质量的同时显著减轻文档负担的潜力。