I agree with Jecker et al that “the headline-grabbing nature of existential risk (X-risk) diverts attention away from immediate artificial intelligence (AI) threats…”1 Focusing on very long-term speculative risks associated with AI is both ethically distracting and ethically dangerous, especially in a healthcare context. More specifically, AI in healthcare is generating healthcare justice challenges that are real, imminent and pervasive. These are challenges generated by AI that deserve immediate ethical attention, more than any X-risk issues in the distant future. Almost (...) 50 years ago, John Knowles edited a volume titled Doing Better and Feeling Worse: Health in the United States. We are ‘doing better’ because numerous advances in medical technologies are saving more lives and improving the quality of our lives. But we are ‘feeling worse’ because the additional costs are unsustainable, are threatening funding for other social goods and are increasing injustices regarding the allocation of those resources. This is precisely the situation we are faced with regarding AI in medicine today. Exploding healthcare costs have generated increasing pressure to control those costs, often with unjust consequences. AI is touted as a critical mechanism for controlling healthcare costs, often with little thought given to justice-relevant consequences. Consider, for example, …. (shrink)

Galasso calls for “the actualization of the public health potential of precision medicine….as the best realistic contribution to health equity” (Galasso 2024, 83). Unfortunately, this is wishful th...

What exactly is a “wicked problem”? It is a social or economic problem that is so complex and so interconnected with other issues that it is extraordinarily difficult or impossible to resolve. This is because all proposed resolutions generate equally complex, equally wicked problems. In this essay, I argue that precision medicine, especially in the context of the U.S. healthcare system, generates numerous wicked problems related to distributive justice. Further, I argue that there are no easy solutions to these wicked (...) problems. The need for trade-offs is inescapable. Rough justice is the best outcome we can hope for, and that outcome requires a commitment to processes of public reason that are fair and inclusive. (shrink)

Many countries currently invest in technologies and data infrastructures to foster precision medicine (PM), which is hoped to better tailor disease treatment and prevention to individual patients. But who can expect to benefit from PM? The answer depends not only on scientific developments but also on the willingness to address the problem of structural injustice. One important step is to confront the problem of underrepresentation of certain populations in PM cohorts via improved research inclusivity. Yet, we argue that the perspective (...) needs to be broadened because the (in)equitable effects of PM are also strongly contingent on wider structural factors and prioritization of healthcare strategies and resources. When (and before) implementing PM, it is crucial to attend to how the organisation of healthcare systems influences who will benefit, as well as whether PM may present challenges for a solidaristic sharing of costs and risks. We discuss these issues through a comparative lens of healthcare models and PM-initiatives in the United States, Austria, and Denmark. The analysis draws attention to how PM hinges on—and simultaneously affects—access to healthcare services, public trust in data handling, and prioritization of healthcare resources. Finally, we provide suggestions for how to mitigate foreseeable negative effects. (shrink)