The potential benefits of generative AI for biomedical visualization are countered by serious concerns about accuracy and potential harm. Researchers warn that the illusion of accuracy created by AI-generated images could lead to misinformed decisions and erode public trust in scientific research.
Biomedical visualization specialists are grappling with the implications of using generative AI tools for creating images in health and science applications. While the technology holds promise for streamlining processes and generating visually compelling content, concerns about accuracy and potential harm are paramount. The potential for generating incorrect anatomical illustrations or perpetuating misinformation in clinical settings or online spaces raises serious ethical and safety concerns.
Researchers from the University of Bergen in Norway, the University of Toronto in Canada, and Harvard University in the US highlight these concerns in their paper, 'It looks sexy but it's wrong.' Tensions in creativity and accuracy using GenAI for biomedical visualization,' set to be presented at IEEE's Vis 2025 conference in November. The authors, Roxanne Ziman, Shehryar Saharan, Gaël McGill, and Laura Garrison, present a side-by-side comparison of images created by OpenAI's GPT-4 or DALL-E 3 and those produced by experienced biomedical visualization specialists. The paper underscores the deceptive realism often achieved by generative AI, stating that 'visuals produced by GenAI often look polished and professional enough to be mistaken for reliable sources of information.' This illusion of accuracy can have dangerous consequences, leading individuals to make critical decisions based on fundamentally flawed representations. While the authors haven't encountered documented cases of AI-generated images directly causing harm in health settings, they cite an example involving an AI-based risk-scoring system that wrongfully accused foreign parents of childcare benefits fraud in the Netherlands. The larger concern, the researchers argue, is the potential for inaccurate imagery to erode public trust in scientific research. Ziman points to instances like the 'well-endowed rat' image, a satirical creation that gained widespread attention and contributed to public cynicism towards scientific research. 'Satirical criticism by such public figures (that people may tend to trust more than ‘legitimate’ news sources) can throw into question the legitimacy of the scientific research community at large, and the public can come to distrust (even more) or not take seriously what they hear coming out of the scientific research community,' Ziman explains. This erosion of trust has significant implications for public health communications, particularly during crises like the COVID-19 pandemic.The paper highlights a survey of 17 biomedical visualization professionals, revealing diverse perspectives on generative AI. Respondents were categorized into five personas based on their attitudes and usage patterns: Enthusiastic Adopters, Curious Adapters, Curious Optimists, Cautious Optimists, and Skeptical Avoiders. While some appreciated the unique aesthetics of AI-generated images, others criticized their generic style and tendency to produce irrelevant or hallucinated details. Despite these concerns, some respondents saw value in using generative AI for tasks like generating code snippets or cleaning data. However, many preferred to rely on their existing coding skills rather than delegate them to AI tools
Healthcare Generative AI Biomedical Visualization Accuracy Ethics Misinformation Public Trust Healthcare
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