National perspectives on artificial intelligence use in cancer care: Confidence, adoption, and barriers.

Abstract

e13648 Background: Artificial intelligence (AI) in cancer care offers opportunities to reduce administrative burden, improve efficiency, and support care delivery, yet it prompts questions around safety, equity, and clinical judgment. To assess real-world perspectives, the Association of Cancer Care Centers (ACCC) conducted a national survey examining how oncology professionals engage with AI, their perceived value, and the barriers to adoption. Methods: ACCC, with guidance from an expert committee, created a national online survey of multidisciplinary staff at US cancer programs from May and August 2025. The 26-item survey assessed experiences and perceptions of AI, organizational adoption and governance, and implementation barriers and facilitators. Completed responses were analyzed using descriptive and stratified statistics in Stata 18, with qualitative data examined using rapid inductive thematic analysis. Results: Respondents (N=168) from 36 states included 61% in care delivery and 39% in administrative/operational roles, primarily from community (46%) and NCI-designated or academic (44%) programs. Care delivery staff were less confident than administrative/operations respondents in describing AI use (21% vs 4% “not at all [confident]”), discussing benefits of AI use (18% vs 7%), and critically evaluating AI systems (26% vs 13%). Administrative/operations respondents reported higher confidence contributing to AI implementation (45% vs 32%) and a higher likelihood to adopt/expand AI for treatment planning (52% vs 34%), clinical trial matching (49% vs 32%), and prior authorization (52% vs 35%). Rural respondents were more likely to express concern about declines in patient-provider communication (63% vs 22% suburban and 30% urban), as were those without AI experience (54% vs 24%). Academic respondents rated improving clinical decision-making/diagnostic accuracy, and establishing performance benchmarks and evaluating AI systems, as higher motivators for integration (0.82 vs 0.41; 0.53 vs 0.26), whereas community respondents rated patient engagement higher (0.36 vs 0.13). Respondents with AI experience reported a higher likelihood to adopt/expand AI for chatbots and virtual assistants (52% vs 16%), real-time alerts (35% vs 14%), personalized patient education (46% vs 27%), and individualized patient navigation (38% vs 11%). Qualitative responses reinforced these findings, highlighting stakeholder engagement, evidence of effectiveness, training, integration, and governance as critical for AI implementation. Conclusions: The results reveal broad AI use despite limited AI tools and governance implementation. Respondents were confident acknowledging AI’s limitations but less confident in evaluating its utility, highlighting opportunities for ACCC to support adoption, governance, and AI integration focused on efficiency and patient outcomes.