Artificial Intelligence in Oncology: A 10-Year ClinicalTrials.gov-Based Analysis Across the Cancer Control Continuum

Abstract

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.

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