Perceived Barriers, Supportive-Care Integration, and Readiness for AI Integration in Oncology Practice: A Cross-Sectional Survey of Cancer Care Providers in Saudi Arabia.

BACKGROUND: Cancer care in Saudi Arabia faces systemic barriers including fragmented electronic health records (EHRs), financial constraints, and workforce shortages. Artificial intelligence (AI) offers potential solutions, yet provider AI readiness in oncology remains poorly characterized, particularly in the Middle East. This study assessed perceived barriers, supportive-care integration, and providers' readiness for AI-driven solutions. METHODS: A cross-sectional survey was distributed electronically to cancer care providers across Saudi Arabia between January 17 and February 25, 2026. The instrument comprised 11 investigator-developed question blocks assessing barriers (9 items, 5-point Likert), supportive-care service availability and referral patterns (9 services), AI perceptions (6 agreement items), AI application usefulness (6 tools), and adoption willingness. Non-parametric tests (Kruskal-Wallis, Mann-Whitney U), Spearman correlations, and ordinal logistic regression were used for inferential analyses. Bonferroni correction was applied for multiple comparisons. RESULTS: Eighty-three providers responded (83.1% physicians; mean experience 13.1 ± 8.7 years). Financial constraints (mean 3.42/5) and EHR fragmentation (3.37/5) were the highest-rated barriers. Psycho-oncology (32.5% easily accessible) and fertility preservation (30.1%) had the largest supportive-care gaps. AI willingness was high (76%), with MDT coordination dashboards rated most useful (mean 3.98). EHR fragmentation correlated positively with AI coordination tool preference (ρ = 0.396, p = 0.0002, surviving Bonferroni correction). Data privacy (72.2%) and algorithm bias (56.9%) were the dominant concerns. In exploratory ordinal logistic regression, overall cancer care rating was the only covariate independently associated with AI willingness (OR = 1.34, 95% CI 1.05-1.72, p = 0.020); a binary sensitivity analysis showed the same direction (AUC = 0.77). AI willingness did not differ significantly by sex, role, or institution type. CONCLUSION: Saudi cancer care providers report systemic barriers alongside high willingness to adopt AI tools, particularly for care coordination. These findings identify MDT dashboards, privacy safeguards, and supportive-care gaps in psycho-oncology and fertility preservation as focal points for Saudi Arabia's Vision 2030 digital health transformation in oncology.