ArticleJournal of medical Internet research2026
AI Adoption in US Cancer Centers: National Cross-Sectional Study of Institutional and Policy Determinants.
Article in Journal of medical Internet research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
5 authors.
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Abstract
Background: AI is increasingly being integrated into cancer screening, treatment, and patient care. However, AI adoption across cancer centers varies, raising concerns about unequal access to AI-enabled cancer care. Objective: This study examined publicly visible AI adoption among National Cancer Institute (NCI)-designated cancer centers in the United States and assessed whether adoption was associated with institutional characteristics, socioeconomic context, geographic distribution, and state-level political environment. Methods: We assembled a national dataset of 75 NCI-designated cancer centers using publicly available sources. AI adoption was measured across 3 domains (screening, treatment, and patient care) and summarized as a composite index (0-3). Spatial clustering was evaluated using Moran Results: Among the 75 cancer centers, the mean AI adoption index was 1.37 (SD 0.86), indicating adoption in approximately 1 to 2 domains on average. Publicly visible AI adoption was most common for screening applications (mean 0.86, SD 0.35), followed by patient care applications (mean 0.50, SD 0.50), and treatment-related applications (mean 0.22, SD 0.42). Moran Conclusions: AI adoption among US cancer centers appears uneven across clinical domains and is more closely associated with institutional capacity than with surrounding community socioeconomic characteristics. The absence of statistically significant spatial clustering suggests that, if AI diffusion is occurring, it may be driven primarily through nonspatial channels, such as institutional resources, academic networks, vendor partnerships, or policy environments, rather than geographic proximity alone. Because the study relies on public-source reporting, the findings should be interpreted as patterns of publicly visible AI adoption rather than confirmed clinical integration. Future research should use longitudinal designs, direct institutional validation, and more detailed implementation measures to assess whether AI-enabled cancer care is diffusing equitably across institutions and populations.
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