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Paper short abstract

This case study of a discontinued AI-based decision aid investigates how actors understood its purpose, role in consultations and development. It approaches failure as an analytic lens to examine how sociotechnical negotiations around expertise, organization and clinical work shape medical practice.

Paper long abstract

AI-based decision aids are frequently framed by developers, tech companies, and healthcare organizations to improve shared decision-making, reduce overtreatment, and enhance patient autonomy. Yet they often struggle to become meaningfully embedded in clinical practice. We conducted a case study of an AI-based decision aid developed in the Netherlands to support patients with prostate cancer by providing personalized information on treatment options and predicted side effects, which, despite high expectations, was ultimately discontinued.

Inspired by Latour’s Aramis, or the Love of Technology, we approach failure as an analytic lens for examining how entangled social, organizational, and technological factors sustain or unravel decision-support AI technologies. After completion of the project, we conducted semi-structured interviews (n=17) with clinicians, AI developers, health scientists, decision-aid company employees, and a patient representative involved. Through reflexive thematic analysis, we trace how actors understood the purpose of the decision aid, its place within clinical consultations, and their roles in its development and use.

Findings show that procedurally, clinicians were asked to integrate additional informational outputs into consultations, which prompted reflections about treatment decisions and care paths. Experientially, the introduction of the decision aid surfaced tensions around expertise, expectations, and boundaries of medical judgment. Organizationally, multi-stakeholder arrangements, regulatory changes, and dependencies on an external company introduced new coordination demands and economic considerations that shaped how the decision aid could be employed.

This study shifts attention to the sociotechnical relations, negotiations, and frictions through which decision-support AI technologies reframe medical practice – dynamics that often remain invisible in successful deployments.