Previously submitted to: JMIR Cancer (no longer under consideration since Sep 08, 2026)
Date Submitted: Aug 29, 2026
Open Peer Review Period: Sep 8, 2026 - Sep 8, 2026
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Public Acceptance of AI-Based Raman Spectroscopy Skin Cancer Screening in Vietnam: Cross-Sectional Stated-Preference Survey
ABSTRACT
Background:
Artificial intelligence (AI)-assisted Raman spectroscopy offers a non-invasive, rapid modality for skin cancer screening, but its public acceptance in low- and middle-income countries remains unexplored. No discrete-choice experiment (DCE) or stated-preference study has examined willingness to undergo AI-Raman screening in Vietnam or Southeast Asia.
Objective:
To quantify public acceptance of AI-based Raman spectroscopy skin cancer screening in Vietnam and identify demographic and attitudinal predictors of screening uptake.
Methods:
A cross-sectional stated-preference survey was administered to 1,890 Vietnamese adults recruited through five channels (LinkedIn, email, Facebook, mail, Zalo). The survey employed a blocked fractional-factorial design with 20 blocks and 14 choice tasks per respondent (26,460 total observations), each presenting two screening alternatives and an opt-out option. Demographic variables (DN1-DN8) and attitude scales measuring skin cancer knowledge (NT), AI attitude (AI), and screening barriers/trust (DST, SST, BAHN, SS, TS) were collected. Logistic regression and multilevel models estimated predictors of screening uptake and choice behavior. Data-quality flags were applied and sensitivity analyses conducted.
Results:
Overall, 90.56% of choices selected a screening option (Alternative 1: 45.67%, Alternative 2: 44.89%), yielding an opt-out rate of 9.44%. The mean number of opt-outs per respondent was 1.32 (SD 1.11, range 0-6); 74.23% of respondents opted out of at least one task, and none opted out of all 14. Acceptance was remarkably uniform across all demographic subgroups (age, sex, education, income, occupation, urbanicity, prior screening experience), with no significant predictor in the multivariable logistic model (pseudo-R² = 0.005, n = 1,890). Skin cancer knowledge score was weakly associated with opt-out (OR = 0.82, p = 0.058), while AI attitude score was not (OR = 0.87, p = 0.337). The alt1-versus-alt2 choice model showed negligible explanatory power from respondent features (pseudo-R² = 0.0001, n = 23,962), confirming that choice between alternatives was driven by task design rather than respondent characteristics. Data quality was high (89.42% clean responses), and opt-out rates were nearly identical between clean and flagged subsets (9.45% vs 9.32%).
Conclusions:
Public acceptance of AI-based Raman spectroscopy skin cancer screening in Vietnam is high and equitable across demographic groups. The uniform acceptance pattern suggests that implementation barriers are more likely to be systemic (infrastructure, cost, regulation) than attitudinal. Full attribute-based utility estimation requires the conjoint design matrix from the survey platform; the present analysis establishes the stated-preference baseline for this population.
Citation
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