Background/Objectives: Artificial Intelligence (AI) is rapidly advancing in medicine, facilitating personalized care by leveraging complex clinical data, imaging, and patient monitoring. This study characterizes current practices in AI use within oncology clinical trials by analyzing completed U.S. trials within the Cancer Control Continuum (CCC), a framework that spans the stages of cancer etiology, prevention, detection, diagnosis, treatment, and survivorship. Methods: This cross-sectional study analyzed U.S.-based oncology trials registered on ClinicalTrials.gov between January 2015 and April 2025. Using AI-related MeSH terms, we identified trials addressing stages of the CCC. Results: Fifty completed oncology trials involving AI were identified; 66% were interventional and 34% observational. Machine Learning was the most common AI application, though specific algorithm details were often lacking. Other AI domains included Natural Language Processing, Computer Vision, and Integrated Systems. Most trials were single-center with limited participant enrollment. Few published results or reported outcomes, indicating notable reporting gaps. Conclusions: This analysis of ClinicalTrials.gov reveals a dynamic and innovative landscape of AI applications transforming oncology care, from cutting-edge Machine Learning models enhancing early cancer detection to intelligent chatbots supporting treatment adherence and personalized survivorship interventions. These trials highlight AI’s growing role in improving outcomes across the CCC in advancing personalized cancer care. Standardized reporting and enhanced data sharing will be important for facilitating the broader application of trial findings, accelerating the development and clinical integration of reliable AI tools to advance cancer care.
背景/目的：人工智能在医学领域快速发展，通过利用复杂的临床数据、影像和患者监测，推动了个性化医疗的实现。本研究通过分析癌症控制连续体框架内已完成的美国临床试验，描述了当前肿瘤学临床试验中人工智能应用的实际状况。该框架涵盖癌症病因学、预防、检测、诊断、治疗及生存期管理等各个阶段。方法：这项横断面研究分析了2015年1月至2025年4月期间在ClinicalTrials.gov上注册的美国肿瘤学试验。通过使用与人工智能相关的MeSH术语，我们识别出涉及癌症控制连续体各阶段的试验。结果：共识别出50项已完成的人工智能相关肿瘤学试验；其中66%为干预性试验，34%为观察性试验。机器学习是最常见的人工智能应用，但具体算法细节往往缺失。其他人工智能领域包括自然语言处理、计算机视觉和集成系统。大多数试验为单中心研究，参与者招募规模有限。仅有少数试验公布了结果或报告了研究结局，显示出显著的报告缺口。结论：对ClinicalTrials.gov的分析揭示了人工智能应用正在改变肿瘤学诊疗的动态创新格局——从提升早期癌症检测的前沿机器学习模型，到支持治疗依从性的智能聊天机器人及个性化生存期干预措施。这些试验突显了人工智能在改善癌症控制连续体各阶段结局、推进个性化癌症治疗方面日益重要的作用。标准化报告和加强数据共享对于促进试验结果的广泛应用、加速可靠人工智能工具的开发和临床整合以推进癌症治疗具有重要意义。
肿瘤(癌症)患者之家