Public governance of medical artificial intelligence research in the UK: an integrated multi-scale model

Abstract

There is a growing consensus among scholars, national governments, and intergovernmental organisations of the need to involve the public in decision-making around the use of artificial intelligence (AI) in society. Focusing on the UK, this paper asks how that can be achieved for medical AI research, that is, for research involving the training of AI on data from medical research databases. Public governance of medical AI research in the UK is generally achieved in three ways, namely, via lay representation on data access committees, through patient and public involvement groups, and by means of various deliberative democratic projects such as citizens’ juries, citizen panels, citizen assemblies, etc.—what we collectively call “citizen forums”. As we will show, each of these public involvement initiatives have complementary strengths and weaknesses for providing oversight of medical AI research. As they are currently utilized, however, they are unable to realize the full potential of their complementarity due to insufficient information transfer across them. In order to synergistically build on their contributions, we offer here a multi-scale model integrating all three. In doing so we provide a unified public governance model for medical AI research, one that, we argue, could improve the trustworthiness of big data and AI related medical research in the future.

Artificial intelligence and medical research databases: ethical review by data access committees

“…This procedural value is important for the ethical oversight of research databases, for if one of the goals of DACs is to maximise the utility of data for public benefit, the question of what constitutes public benefit is one that, procedurally, requires broad public deliberation to determine. Multiple mechanisms exist for deliberating about collective values for AI, from online crowd sourcing to citizen forums [ 40 – 42 ]. Public involvement is a long-standing complementary mechanism to those processes and can continue to be useful within the specific local contexts of research applications.…”

“…When examined more closely, however, it becomes apparent that PPI members represent a diverse range of subject positions and collective interests. These may include the general interests of patients as a whole, the specific interests of particular patient communities (such as cancer patients), the interests of community groups defined by demographic characteristics (such as ethnicity, age, or gender), and the broader interests of citizens [ 40 ]. The reason they can do this is because they have cultural knowledge about collective interests, which provides them with acquired vigilance allowing them to anticipate relevant community harms and benefits.…”

Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education

“…This includes understanding the potential legal consequences of AI failures or malfunctions, and who bears the responsibility in such situations. Engaging institutional review boards leverages their expertise in ethical review processes, particularly when handling sensitive data or novel applications of AI [ 59 ]. Regular collaboration with these ethical oversight bodies ensures that any emerging ethical concerns are identified and addressed promptly [ 60 ].…”

Australian women's judgements about using artificial intelligence to read mammograms in breast cancer screening

“…This is reflected in recent calls for greater PPI in healthcare AI development and implementation. 6 , 7 Reading mammograms in breast cancer screening programmes is a relatively advanced AI use case. 3 In Australia, women aged 50–74 are offered publicly funded breast screening every 2 years via the national BreastScreen program; women aged 40–49 can access screening.…”