Developer Perspectives on Potential Harms of Machine Learning Predictive Analytics in Health Care: Qualitative Analysis.

ABSTRACT:

Background

Machine learning predictive analytics (MLPA) is increasingly used in health care to reduce costs and improve efficacy; it also has the potential to harm patients and trust in health care. Academic and regulatory leaders have proposed a variety of principles and guidelines to address the challenges of evaluating the safety of machine learning-based software in the health care context, but accepted practices do not yet exist. However, there appears to be a shift toward process-based regulatory paradigms that rely heavily on self-regulation. At the same time, little research has examined the perspectives about the harms of MLPA developers themselves, whose role will be essential in overcoming the "principles-to-practice" gap.

Objective

The objective of this study was

SUBMITTER: Nichol AA 

PROVIDER: S-EPMC10690528 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature