Managing a "responsibility vacuum" in AI monitoring and governance in healthcare: a qualitative study.

ABSTRACT:

Background

Despite the increasing implementation of artificial intelligence (AI) and machine learning (ML) technologies in healthcare, their long-term safety, effectiveness, and equity remain compromised by a lack of sustained oversight. This study explores the phenomenon of a "responsibility vacuum" in AI governance, wherein maintenance and monitoring tasks are poorly defined, inconsistently performed, and undervalued across healthcare systems.

Methods

We conducted semi-structured interviews with 21 experts involved in AI implementation in healthcare, including clinicians, clinical informaticists, computer scientists, and legal/policy professionals. Participants were recruited through purposive and snowball sampling. Interviews were transcribed, coded, and analyzed using ab

SUBMITTER: Owens K 

PROVIDER: S-EPMC12482494 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature