Visualizing cancer and survivorship with generative AI?-an exploration of breast, prostate, and pancreatic cancer imagery.

IF 3.1 2区 医学 Q2 ONCOLOGY
Miguel Varela-Rodríguez, Stefanie Plage
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引用次数: 0

Abstract

Purpose: Generative Artificial Intelligence (GAI) is transforming visual communication in the context of cancer survivorship, presenting opportunities to innovate advocacy while also posing risks for social representation. This study explores how GAI visualizes cancer and survivorship, focusing on its ability to reflect diverse experiences and its limitations.

Methods: We analyzed 262 images generated by Dall-E and Stable Diffusion using prompts related to breast, prostate, and pancreatic cancer. A mixed-methods approach examines how GAI utilizes cancer signifiers, visualizes the impact of cancer on individuals, and represents people with cancer.

Results: GAI frequently reproduces cancer tropes, such as prescriptive positivity, and fails to depict medical treatments or embodied experiences unless explicitly prompted. AI-generated images predominantly featured White, female subjects, particularly in breast cancer contexts, reflecting broader biases in public discourse. While GAI tools can produce inclusive visuals, achieving this requires users to have nuanced knowledge of cancer and survivorship, limiting accessibility for lay GAI users.

Conclusions: GAI can support cancer communication but risks perpetuating stereotypes and excluding less visible experiences of cancer. Our findings offer practical insights to support the design of advocacy materials and campaigns, particularly through improved prompt literacy and inclusive image generation strategies.

Implications for cancer survivors: Inclusive and respectful visual representation is critical for capturing the diverse realities of cancer survivorship, which in turn affects the wellbeing of cancer survivors and carers. Collaborative efforts among researchers, advocates, and GAI developers are necessary to improve datasets and foster accessible tools, ensuring that GAI supports rather than undermines cancer survivorship advocacy.

用生成式人工智能可视化癌症和幸存者?-探索乳腺癌、前列腺癌和胰腺癌的影像。
目的:生成式人工智能(GAI)正在改变癌症幸存者背景下的视觉传播,为创新倡导提供了机会,同时也为社会代表性带来了风险。本研究探讨了GAI如何可视化癌症和幸存者,重点关注其反映不同经验的能力及其局限性。方法:我们分析了262张Dall-E和Stable Diffusion生成的图像,使用与乳腺癌、前列腺癌和胰腺癌相关的提示。混合方法方法检查GAI如何利用癌症符号,可视化癌症对个人的影响,并代表癌症患者。结果:GAI经常复制癌症比喻,如规定性阳性,除非明确提示,否则无法描述医学治疗或具体体验。人工智能生成的图像主要以白人女性为主题,特别是在乳腺癌背景下,反映了公共话语中更广泛的偏见。虽然GAI工具可以产生包容性的视觉效果,但要实现这一点,需要用户对癌症和幸存者有细微的了解,这限制了GAI外行用户的可访问性。结论:GAI可以支持癌症交流,但有可能使刻板印象永久化,并排除不太明显的癌症经历。我们的研究结果为支持宣传材料和宣传活动的设计提供了实际见解,特别是通过提高快速扫盲和包容性形象生成策略。对癌症幸存者的影响:包容和尊重的视觉表现对于捕捉癌症幸存者的多样化现实至关重要,这反过来又影响癌症幸存者和护理人员的福祉。研究人员、倡导者和GAI开发人员之间的合作努力是必要的,以改进数据集和促进可访问的工具,确保GAI支持而不是破坏癌症幸存者的倡导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.00
自引率
10.80%
发文量
149
审稿时长
>12 weeks
期刊介绍: Cancer survivorship is a worldwide concern. The aim of this multidisciplinary journal is to provide a global forum for new knowledge related to cancer survivorship. The journal publishes peer-reviewed papers relevant to improving the understanding, prevention, and management of the multiple areas related to cancer survivorship that can affect quality of care, access to care, longevity, and quality of life. It is a forum for research on humans (both laboratory and clinical), clinical studies, systematic and meta-analytic literature reviews, policy studies, and in rare situations case studies as long as they provide a new observation that should be followed up on to improve outcomes related to cancer survivors. Published articles represent a broad range of fields including oncology, primary care, physical medicine and rehabilitation, many other medical and nursing specialties, nursing, health services research, physical and occupational therapy, public health, behavioral medicine, psychology, social work, evidence-based policy, health economics, biobehavioral mechanisms, and qualitative analyses. The journal focuses exclusively on adult cancer survivors, young adult cancer survivors, and childhood cancer survivors who are young adults. Submissions must target those diagnosed with and treated for cancer.
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