AI and Medical Images: Addressing Ethical Challenges to Provide Responsible Access to Historical Medical Illustrations

This article examines the ethical considerations and broader issues around access to digitised historical medical images. These illustrations and, later, photographs are often extremely sensitive, representing disability, disease, gender, and race in potentially harmful and problematic ways. In particular, the original metadata for such images can include demeaning and sometimes racist terms. Some of these images show sexually explicit and violent content, as well as content that was obtained without informed consent. Hiding these sensitive images can be tempting, and yet, archives are meant to be used, not locked away. Through a series of interviews with 10 archivists, librarians, and researchers based in the UK and US, the authors show that improved access to medical illustrations is essential to produce new knowledge in the humanities and medical research, as well as to bridge the gap between historical and modern understandings of the human body. Improving access to medical illustration can also help to address the gender data gap, which has acquired mainstream visibility thanks to the work of activists such as Caroline Criado-Perez, the author of Invisible Women: Data Bias in a World Designed for Men. Users of historical medical archives are therefore a diverse group, which includes researchers in medicine, history, and medical and digital humanities, as well as artists, journalists, and activists. In order to improve discoverability and facilitate access to these archives in an ethical way, this article highlights the importance of appropriate metadata, which can be enhanced through the use of artificial intelligence (AI) tools. Indeed, AI can be used to create new metadata when original information is incomplete or is missing altogether, or when it includes problematic language. AI can also help with the disaggregation of data by gender and/or racial ethnicity. Moreover, it can recommend similar images to allow users to explore other parts of the collections. However, AI can also pose issues, for example when it suggests inappropriate metadata or similarity search results. Keeping humans in the loop is therefore essential when applying AI to sensitive medical images. Ultimately, this article argues that access to sensitive images cannot be separated from responsibility. Recommendations are made to help cultural heritage institutions find the right balance, to provide access for research and education, and to also protect children and other vulnerable audiences from encountering images that can be described as shocking and even traumatising.