Background and Objectives: Artificial intelligence (AI) tools promise efficiency gains in oncology, yet adoption depends on economic factors that remain under-characterized in Eastern European health systems. We quantified AI economic literacy, cost-effectiveness perceptions, reimbursement readiness, and return-on-investment (ROI) confidence among Romanian oncology professionals; we described candidate economic adoption profiles and examined whether sector was associated with the strength of the indirect association between literacy and willingness to invest via ROI confidence. Materials and Methods: A multicenter cross-sectional survey (N = 108) was conducted between September 2025 and April 2026 at “Victor Babes” University of Medicine and Pharmacy Timisoara and affiliated oncology services. Participants completed a 25-item AI Economic Literacy Index (AIELI; 0–25) plus 1–5 scales for ROI confidence, willingness to invest, perceived financial barriers, cost-effectiveness perception, and adoption intention. Analyses used Spearman correlations, multivariable logistic regression, k-means clustering, and covariate-adjusted moderated mediation with 5000 bootstrap resamples. Results: Mean age was 41.3 ± 10.7 years; 58.3% were female. AIELI was moderate (13.7 ± 4.6/25). Familiarity favored cost-effectiveness analysis (59.3%) over AI-specific reimbursement codes (16.7%). High willingness to invest occurred in 48.1% and was independently associated with higher AIELI (aOR 1.78 per +1 SD; 95% CI 1.17–2.71), higher ROI confidence (aOR 1.93; 1.24–3.02), lower perceived financial barriers (aOR 0.58; 0.37–0.91), and prior AI training (aOR 2.34; 1.13–4.86). Three exploratory profiles were identified: Cost-Conscious Adopters (n = 43), Reimbursement-Cautious (n = 37), and Budget-Constrained Skeptics (n = 28). Moderated-mediation models were consistent with a sector-conditional indirect association, largest in private clinics (β = 0.193; 95% CI 0.087–0.318) and weakest in public hospitals (β = 0.072; 0.014–0.158). Conclusions: In this exploratory cross-sectional sample, AI economic literacy and ROI confidence were associated with willingness to invest in oncology AI, interpreted as stated support for investment rather than an enacted procurement decision, since many respondents lacked formal budgetary authority. Because the design cannot establish temporal ordering, whether sector-tailored capability-building and reimbursement clarity would increase adoption remains a hypothesis for prospective evaluation.

We examined whether baseline artificial intelligence (AI) economic literacy was associated with sustained use of oncology AI tools. This prospective cohort included 96 oncology and critical care professionals at two centres in Timișoara, Romania, followed for 12 months after institutional roll-out. Participants were classified by AI Economic Literacy Index (AIELI) scores as high (≥14) or low (<14). Sustained use required log-verified activity in at least two of the final three months. Analyses included regression, inverse probability of treatment weighting and exploratory indirect-association analysis through return-on-investment (ROI) confidence. Among 91 completers, sustained use occurred in 29/53 high-literacy participants (54.7%) and 7/38 low-literacy participants (18.4%; risk ratio 2.97, 95% confidence interval 1.46–6.06). The weighted risk ratio was 2.24 (1.15–4.34), and the profession-stratified risk ratio was 2.80 (1.36–5.76). Median time to first use was 5.8 months in the high-literacy group and was not reached in the low-literacy group. Nine participants (eight high-literacy, one low-literacy) had recorded use before the baseline assessment; in a delayed-entry analysis restricted to the 87 participants unused at assessment, the crude hazard ratio for subsequent first use was 2.98 (1.62–5.48) and the crude risk ratio for sustained use after excluding prior users was 3.01 (1.36–6.64); the corresponding covariate-adjusted estimates were less precise (hazard ratio 1.81, 0.89–3.69; risk ratio 2.19, 0.99–4.86) and their confidence intervals included the null. Roll-out-based time-to-first-use comparisons are descriptive because they include events preceding measurement. Sustained use increased across literacy bands. An indirect association through ROI confidence was observed but was not interpretable as causal mediation. Higher baseline economic literacy was associated with earlier and more sustained recorded AI-tool use, although opportunity for use differed by profession, and the adjusted timing-corrected estimates leave the independent contribution of literacy uncertain. Larger multicentre studies should evaluate whether literacy training improves adoption and whether adoption translates into clinical or economic benefit.