Towards clinical implementation of artificial intelligence in cancer care: Concept mapping analysis of provincial workshop findings

BackgroundArtificial intelligence (AI) has rapidly garnered interest in healthcare. Cancer cares multidisciplinary nature and high coordination demands are well positioned to benefit from AI. While attitudes toward implementation of AI in medicine have been explored generally, literature remains scarce with specific regards to AI in cancer care. This study sought to understand perspectives of both patients and professionals in guiding responsible, effective implementation of evidence-based AI in cancer care. MethodsWe conducted a workshop at a provincial Cancer Summit (Vancouver, Canada). Discussions addressed concerns, benefits, and priorities for AI in cancer care. Responses from 48 workshop participants underwent structured conceptualization by concept mapping. Sorting and rating of the resulting statements were performed by a purposively sampled 13-member expert panel. Multidimensional scaling, hierarchical cluster and subcluster analysis produced visual and quantitative maps of findings. ResultsA total of 265 statements on perceived benefits, concerns, and priorities related to the implementation of AI in cancer care were generated; a deduplicated and consolidated final set of 100 statements underwent concept mapping. Two main clusters identified pertained to "Challenges and Safeguards for AI Implementation " (Cluster 1) and "Clinical Benefits and Efficiency Gains" (Cluster 2). Subcluster analysis distinguished 8 thematic subclusters (4 per cluster). Mean importance and feasibility ratings were higher for Cluster 2, with large effect sizes for both importance (Cohens d = 0.94) and feasibility (Cohens d = 1.59). Ratings by clinical and nonclinical professionals were similar across comparisons except for Cluster 2 feasibility, rated higher by clinical participants (P = .029, Hedges g = 0.465). Further go-zone analysis classified statements according to their relative superiority/inferiority in importance and feasibility from overall average. ConclusionsExpert panel ratings were higher for statements describing clinical benefits and efficiency gains than for those describing challenges and safeguards for AI implementation in cancer care. Concept mapping analysis distinguished between workflow-aligned AI applications, perceived as ready for implementation, and system-level governance requirements requiring longer-term investment. Present findings offer initial, context-specific structuring of stakeholder perspectives that may inform the prioritization and sequencing of AI implementation efforts in cancer care, providing a foundation for validation in other settings. Contributions to the literatureO_LIWhile systematic reviews have identified barriers and facilitators to AI adoption in healthcare, this study provides the first structured prioritization framework for AI implementation in cancer care using concept mapping methodology, addressing an urgent need as AI capabilities advance rapidly in oncology. C_LIO_LIStrategic-level stakeholders distinguished between workflow-aligned domains perceived as implementation-ready and system-level domains requiring longer-term investment, enabling sequenced implementation planning that matches organizational readiness. C_LIO_LIThis approach, combining broad stakeholder statement generation with expert panel prioritization, demonstrates how integrating experiential knowledge with quantitative prioritization methods can provide implementation scientists and health system leaders with actionable frameworks for staged AI adoption strategies. C_LI