Testing domain knowledge and risk of bias of a large-scale general artificial intelligence model in mental health.

ABSTRACT:

Background

With a rapidly expanding gap between the need for and availability of mental health care, artificial intelligence (AI) presents a promising, scalable solution to mental health assessment and treatment. Given the novelty and inscrutable nature of such systems, exploratory measures aimed at understanding domain knowledge and potential biases of such systems are necessary for ongoing translational development and future deployment in high-stakes healthcare settings.

Methods

We investigated the domain knowledge and demographic bias of a generative, AI model using contrived clinical vignettes with systematically varied demographic features. We used balanced accuracy (BAC) to quantify the model's performance. We used generalized linear mixed-effects models to quantify t

SUBMITTER: Heinz MV 

PROVIDER: S-EPMC10123874 | biostudies-literature | 2023 Jan-Dec

REPOSITORIES: biostudies-literature