e13663 Background: Artificial intelligence (AI) in oncology, including computer vision and radiomics, has expanded rapidly with substantial federal support. However, the extent to which growth in funding and publications is accompanied by growth in prospective interventional evaluation is not well quantified. We conducted an ecosystem-level analysis to compare temporal trends in federal awards, peer-reviewed publications, and interventional clinical trial registrations in AI-oncology. Methods: We performed a cross-sectional landscape analysis spanning 2015–2025 using three public data sources: (1) federally funded extramural awards indexed in NIH RePORTER (including NIH/NCI and other agencies where available); (2) interventional studies registered on ClinicalTrials.gov; and (3) PubMed-indexed publications. Records were identified using a consistent Boolean query requiring AI-related terms (“artificial intelligence,” “machine learning,” “deep learning,” “neural network,” “large language model,” “computer vision,” “natural language processing,” “radiomics”) AND oncology-related terms (“cancer,” “oncology,” “tumor,” “neoplasm,” “carcinoma,” “melanoma,” “leukemia,” “lymphoma”). Annual absolute counts were extracted (without normalization) to preserve volume differences across the ecosystem. We summarized growth over time and calculated publication-to-trial and grant-to-output ratios. Results: Between 2015 and 2025, AI-oncology federal awards totaled 11,503. PubMed-indexed AI-oncology publications increased 38-fold from 558 (2015) to 21,414 (2025), totaling 81,782. Interventional AI-oncology trial registrations increased from 2 (2015) to 84 (2025), totaling 372 across the period. Overall, the publication-to-interventional-trial ratio was 220:1; interventional trial registrations represented 0.45% of publication volume. For every federal award, approximately 7 publications were produced, but only 0.03 interventional trials were registered. Conclusions: Across 2015–2025, AI-oncology funding and publications increased dramatically, while interventional trial registrations remained comparatively sparse. This persistent publication-to-trial gap highlights a translational bottleneck and supports targeted initiatives to accelerate prospective validation, such as dedicated funding mechanisms, multi-center evaluation networks, and trial-ready implementation infrastructure, to realize clinical benefit from AI in cancer care.
Vinh et al. (2026) studied this question.