Alcohol consumers' receptivity to artificial intelligence-generated alcohol-cancer risk messages: An experimental study.

Clinical Snapshot

Key Findings

• P Value: p < 0.05 for all significant comparisons

• Effect Size: β = 0.10-0.19 for cancer-specific vs general messages; β = 0.19-0.22 for graphic vs neutral images

• Primary Outcome: Cancer-specific messages produced greater attention, emotional reactions, and intentions to reduce drinking than general messages

• Nnt Or Sensitivity: Not calculable from available data; effect sizes suggest small to moderate improvements in message receptivity

• Confidence Interval: 95% confidence intervals mentioned but not specified in abstract

Clinical Application

AI-generated health messages appear feasible for implementation in digital health platforms and clinical settings Relevant to Australian alcohol harm reduction strategies; aligns with NHMRC alcohol guidelines emphasising cancer risk communication; applicable to GP consultations and public health campaigns Adult alcohol consumers seeking health information, particularly moderate drinkers who showed stronger responsiveness

Abstract

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. Many people in the United States are unaware that drinking alcohol increases the risk of several cancers. This study tested different ways of communicating the alcohol–cancer link, including text‐only messages, messages with artificial intelligence–generated images (neutral or graphic), and general versus cancer‐specific content; in addition, the authors examined whether responses differed between moderate drinkers and heavy drinkers. The authors observed that messages with images were more effective than those with text alone, especially when the images clearly showed health harm. Messages that named specific cancers (such as breast or colon cancer) were also more effective than general statements about cancer; however, heavy drinkers, who face the highest cancer risk, were less responsive overall. The findings suggest that effective alcohol–cancer communication should carefully consider what the message says, how it is presented, and who the audience is. Nonstigmatizing, harm‐reduction messages that encourage small, realistic changes and are delivered in supportive settings, such as conversations with health care providers, may work better for heavy drinkers. The study also highlights the promise of artificial intelligence as a tool for developing and tailoring health messages. Future research should explore how different messages influence behavior over time.

References

1. 1.Asfar, T., Brown, E. C., Penedo, F. J., Abrams, I., Jayakumaran, J., Oluwole, O. J., Ferdous, T., Naimi, T., Avendano, D., & Maziak, W. (2026). Alcohol consumers' receptivity to artificial intelligence-generated alcohol-cancer risk messages: An experimental study. Cancer. https://doi.org/10.1002/cncr.70491