Responsibility and Regulation: Exploring Social Measures of Trust in Medical AI

TL;DR

This paper examines social trust in medical AI systems, highlighting practical challenges in responsible innovation and governance, and discussing how responsibility is distributed in deploying autonomous medical systems.

Contribution

It offers an analysis of expert perspectives on responsible innovation and governance challenges in deploying AI in medical devices, contributing to the discourse on trustworthy autonomous systems.

Findings

Identifies governance and regulatory challenges in medical AI deployment.

Highlights the importance of responsible innovation in medical AI.

Discusses the distribution of responsibility in AI deployment processes.

Abstract

This paper explores expert accounts of autonomous systems (AS) development in the medical device domain (MD) involving applications of artificial intelligence (AI), machine learning (ML), and other algorithmic and mathematical modelling techniques. We frame our observations with respect to notions of responsible innovation (RI) and the emerging problem of how to do RI in practice. In contribution to the ongoing discourse surrounding trustworthy autonomous system (TAS) [29], we illuminate practical challenges inherent in deploying novel AS within existing governance structures, including domain specific regulations and policies, and rigorous testing and development processes, and discuss the implications of these for the distribution of responsibility in novel AI deployment.