Differing perspectives on artificial intelligence in mental healthcare among patients: a cross-sectional survey study.

Introduction: Artificial intelligence (AI) is being developed for mental healthcare, but patients' perspectives on its use are unknown. This study examined differences in attitudes towards AI being used in mental healthcare by history of mental illness, current mental health status, demographic characteristics, and social determinants of health.

Methods: We conducted a cross-sectional survey of an online sample of 500 adults asking about general perspectives, comfort with AI, specific concerns, explainability and transparency, responsibility and trust, and the importance of relevant bioethical constructs.

Secondary Qualitative Analysis of Stigmatizing and Nonstigmatizing Language Used in Hospital Birth Settings.

Objective: To more clearly understand the use of stigmatizing and nonstigmatizing language in electronic health records in hospital birth settings and to broaden the understanding of discrimination and implicit bias in clinical care.

Design: A secondary qualitative analysis of free-text clinical notes from electronic health records.

Setting: Two urban hospitals in the northeastern United States that serve patients with diverse sociodemographic characteristics during the perinatal period.

Identifying stigmatizing and positive/preferred language in obstetric clinical notes using natural language processing.

Objective: To identify stigmatizing language in obstetric clinical notes using natural language processing (NLP).

Materials And Methods: We analyzed electronic health records from birth admissions in the Northeast United States in 2017. We annotated 1771 clinical notes to generate the initial gold standard dataset.