Currently submitted to: Journal of Medical Internet Research
Date Submitted: Mar 26, 2026
Open Peer Review Period: Mar 27, 2026 - May 22, 2026
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Institutional and Policy Determinants of Artificial Intelligence Adoption in U.S. Cancer Centers: A National Analysis of Digital Health Diffusion
ABSTRACT
Background:
Background:
Artificial intelligence (AI) is quickly becoming a key part of digital health systems in oncology, supporting activities like cancer screening, clinical decision-making, and patient care management. Although AI has the potential to enhance care quality and efficiency, its adoption at cancer centers varies widely, raising concerns about disparities in digital health access and capacity.
Objective:
Objective:
This research investigates the multiple factors influencing AI adoption as part of digital health implementation at National Cancer Institute (NCI)-designated cancer centers across the U.S., focusing on institutional readiness, policy environment, and geographic spread.
Methods:
Methods:
A national dataset of 75 cancer centers was assembled using public sources to track AI use in screening, treatment, and patient care. AI adoption was measured as a composite index (0-3), indicating integration across clinical areas. Spatial patterns were analyzed with Moran’s I, and multilevel ordered logistic regression models examined links between AI adoption, institutional features (like number of physicians, hospital beds, center type), and contextual factors (such as socioeconomic status and state politics).
Results:
Results:
No significant clustering of AI adoption was found geographically, implying limited regional diffusion. The size of the physician workforce was the most consistent predictor of AI adoption, emphasizing that organizational readiness is a key driver. Policy environment also influenced adoption: comprehensive cancer centers in Republican-controlled states showed higher AI uptake. Socioeconomic status at the community level was not significantly related.
Conclusions:
Conclusions:
This study identifies institutional capacity and policy environment as primary constraints on scalable innovative digital health implementation in cancer institutions. These results point to structural barriers to broad digital health deployment and indicate that advancing AI-enabled cancer treatment will need focused investments in institutional capacity and policy support. Without these efforts, disparities in digital health infrastructure could restrict equitable access to AI-driven innovations in oncology.
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Copyright
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