ArticleCancer2026
Alcohol consumers' receptivity to artificial intelligence-generated alcohol-cancer risk messages: An experimental study.
Article in Cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
10 authors.
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Abstract
backgroundAlcohol 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.
methodsEight 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).
resultsCancer-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).
conclusionsArtificial 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.
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