Congratulations to Prof. Daniel Weissglass for publishing a paper “AI preference prediction beyond substituted judgement: enhancing best interest decision-making” in a leading medical ethics journal, Journal of Medical Ethics, co-authored with students, based on a project begun during a course.
It is the product of a research-oriented teaching method Prof. Weissglass uses in applied ethics courses, which asks students to work as part of a research team with Prof. Weissglass in exploring cutting-edge ethical questions. This paper began in global health ethics, and was supported in further development by the SRS program. It resulted in a presentation at a professional workshop in Singapore before ultimately being prepared for successful publication in the Journal of Medical Ethics, which is arguably the most impactful journal in the field. It includes six coauthors who were enrolled in that course – some of whom are still at DKU.
Abstract
Tracking patient preferences is vital to medical decision-making, but evidence suggests that the standard method for tracking the preferences of incapacitated or incompetent patients (ie, surrogates) is inaccurate. Recent proposals suggest that artificial intelligence preference predictors (AIPPs) can improve preference tracking for these patients, but have faced significant objections. While many of these objections depend on unsettled empirical or technical assumptions, one prominent objection—that AIPPs rely inappropriately on impersonal information—seems to be an in-principle challenge to AIPPs. In this paper, we show that even granting an implausibly strong version of this objection, AIPPs may provide value to clinical decision-making. To show this, we develop suggestions that AIPPs may support best interest decision-making (BIDM) by improving the accuracy, consistency and speed of BIDM, and show that the prevalence of BIDM in the intensive care unit (ICU) grants this application of AIPPs significant moral and practical consequence. This not only clears a path to improve BIDM but also establishes a safe harbour—a relatively uncontroversial yet impactful space—in which proponents may develop AIPPs sufficently to resolve empirical and technical questions about their potential. We conclude by highlighting key questions for the application of AIPPs to BI determinations, setting an agenda for the deeper examination of a largely overlooked application of these tools.