Comparing Images of Depression in Mass Media and AI-Generated Pictures: Mixed Methods Study
Clinical Snapshot
PICO Framework
| P — Population | People with depression and young people (807 total participants: 15 in discussion groups, 792 in online survey) |
| I — Intervention | Exposure to AI-generated images depicting depression |
| C — Comparator | Mass media images depicting depression |
| O — Outcomes | Perceptions of stereotyping, stigmatisation, appropriateness, realism, inclusivity, and potential negative impact on people with depression |
Bottom Line
This mixed-methods study suggests that AI-generated images of depression are perceived as more stereotypical and stigmatising than traditional mass media representations. While the research addresses an important contemporary issue, the abstract provides insufficient methodological detail to fully assess study quality. Key limitations include lack of information about sampling methods, outcome measurement validation, and statistical precision beyond P-values. The findings may have implications for mental health communication strategies, particularly as AI-generated content becomes more prevalent. However, clinicians should interpret these results cautiously given the limited methodological transparency and unclear generalisability to Australian populations.
Key Findings
P Value: P<0.001 for differences between control and test groups
Effect Size: Not reported
Primary Outcome: Participants rated AI-generated images as more stereotypical and stigmatising compared to mass media images
Nnt Or Sensitivity: Not applicable for this study design
Confidence Interval: Not reported
Clinical Application
Findings suggest need for careful consideration when using AI-generated imagery in mental health contexts Relevant to Australian mental health organisations and media outlets, particularly given national focus on reducing mental health stigma through initiatives like R U OK? Day and beyondblue campaigns Mental health professionals, science communicators, and organisations involved in mental health awareness campaigns
Abstract
BACKGROUND: Images play an important role in reducing stigma related to mental health, which often is distorted in the media. In recent years, generative artificial intelligence (AI) has been used to generate images related to mental health. However, first reports suggest that AI-generated images do not depict mental health conditions accurately. In-depth studies on the topic of mental health representations in AI-generated images are still missing. OBJECTIVE: The main objective of this study is to analyze and compare the visual representation of depression in mass media and in AI-generated images. METHODS: The methodologies used were discussion groups (15 participants) and a quasi-experimental online survey (792 interviewees), aimed at people with depression and young people. RESULTS: The results showed that both the images used in the media and those generated by AI reproduced stereotypes and stigmas about depression. However, participants considered AI-generated pictures to be more stereotypical, stigmatizing, and more likely to have a negative impact on people with depression. In contrast, media images were considered more appropriate, realistic, inclusive, and that better reflected the relationship between gender and depression. Statistically significant differences were observed between the control and test groups in both people with depression and young people (P<.001), indicating that when people were aware of what images were AI-generated, they tended to reject them to a greater extent. CONCLUSIONS: Considering the current trend toward the widespread use of AI in mental health communication, it is crucial to promote closer collaborations between science journalists, AI developers, and mental health experts, including patients' associations, as well as a shift toward user-participatory AI design.
References
- 1.Saladie, N., Llorente, C., Joosten, R., & Revuelta, G. (2026). Comparing images of depression in mass media and AI-generated pictures: Mixed methods study. JMIR Human Factors. https://doi.org/10.2196/81230
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