大模型助力肺癌筛查诊断，提升个性化治疗水平
A Narrative Review on Large AI Models in Lung Cancer Screening, Diagnosis, and Treatment Planning
- 按模态分类大模型，整合图像与临床数据
- 在LIDC-IDRI等数据集上实现精准结节检测
- 适合临床医生与AI医疗研究者参考
肺癌仍是全球最常见且致命的疾病之一，亟需准确及时的诊断与治疗。近年来，大模型在医学影像理解与临床决策中取得显著进展。本文系统综述了大模型在肺癌筛查、诊断、预后及治疗规划中的最新应用，将现有模型分为模态专用编码器、编码器-解码器框架与联合编码架构，重点介绍CLIP、BLIP、Flamingo、BioViL-T和GLoRIA等代表性模型。通过LIDC-IDRI、NLST和MIMIC-CXR等基准数据集评估其在多模态学习任务中的表现，涵盖肺结节检测、基因突变预测、多组学整合与个性化治疗规划，并呈现初步的临床部署与验证证据。最后分析通用性、可解释性与合规性等局限，提出构建可扩展、可解释、临床集成化AI系统的未来方向。本综述凸显了大模型在推动肺癌诊疗个性化与优化方面的变革潜力。
原文摘要 · Abstract (English)
Lung cancer remains one of the most prevalent and fatal diseases worldwide, demanding accurate and timely diagnosis and treatment. Recent advancements in large AI models have significantly enhanced medical image understanding and clinical decision-making. This review systematically surveys the state-of-the-art in applying large AI models to lung cancer screening, diagnosis, prognosis, and treatment. We categorize existing models into modality-specific encoders, encoder-decoder frameworks, and joint encoder architectures, highlighting key examples such as CLIP, BLIP, Flamingo, BioViL-T, and GLoRIA. We further examine their performance in multimodal learning tasks using benchmark datasets like LIDC-IDRI, NLST, and MIMIC-CXR. Applications span pulmonary nodule detection, gene mutation prediction, multi-omics integration, and personalized treatment planning, with emerging evidence of clinical deployment and validation. Finally, we discuss current limitations in generalizability, interpretability, and regulatory compliance, proposing future directions for building scalable, explainable, and clinically integrated AI systems. Our review underscores the transformative potential of large AI models to personalize and optimize lung cancer care.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。