Alcohol consumers’ receptivity to artificial intelligence–generated alcohol–cancer risk messages: An experimental study
Menée en 2025 aux Etats-Unis auprès de 639 adultes consommant modérément ou excessivement de l'alcool, cette étude analyse l'effet, sur leurs réactions et leurs comportements, de 8 messages textes fondés sur des preuves ainsi que 2 messages visuels générés par l'intelligence artificielle concernant les risques généraux et spécifiques au cancer associés à la consommation d'alcool
Background: Alcohol is a group 1 carcinogen linked to seven cancers, yet awareness of this risk remains low in the United State. Identifying effective alcohol–cancer communication strategies is a public health priority. The objective of this study was to test the effects of message specificity (general vs. cancer-specific) and visual intensity (text-only, neutral pictorial, graphic pictorial) on message receptivity (attention, emotional reactions) and precursors of behavior (harm perception; intentions to reduce, limit, or stop drinking) among moderate and heavy alcohol consumers.
Methods: Eight evidence-based text messages were created across two topics: general and cancer-specific harm. Artificial intelligence was used to generate pictorial versions at two intensities: neutral (symbolic, no visible disease) and graphic (explicit health consequences). In a 2025 online within-subject/between-subject crossover experiment, 639 US adult consumers (aged 21 years and older; 49.3% female) each viewed two randomly selected messages (one general, one cancer-specific) presented in three formats (text-only, neutral, and graphic; for six total formats), with counterbalanced order. Linear mixed-effects models were used to estimate differences by intensity, specificity, and drinking levels, reporting regression coefficients (β, 95% confidence intervals).
Results: Cancer-specific messages produced greater attention, emotional reactions, and intentions to reduce drinking than general messages (β = 0.10–0.19; p < .05). Graphic pictorials outperformed neutral images on emotional and behavioral outcomes (β = 0.19–0.22; p < .05). Moderate consumers showed stronger perceived harm and message responsiveness than heavy consumers (β = 0.29–0.77; p < .05).
Conclusions: Artificial intelligence–generated alcohol–cancer messages are feasible and effective in strengthening precursors to behavior change. Cancer-specific content and higher visual intensity enhance impact, particularly among moderate consumers, highlighting the importance of tailoring alcohol–cancer communication strategies to different audience characteristics.
Cancer , article en libre accès, 2026