March 28 (Reuters) - Analyzing social media using

artificial intelligence may pick up signals of depression in

white Americans but not in Black counterparts, according to a

study that highlights the risk of training AI models for

healthcare-related tasks without data from diverse racial and

ethnic groups.

The AI model used for the study was more than three times

less predictive for depression when applied to Black people who

use Meta Platforms' ( META ) Facebook than for white people, the

researchers reported.

"Race seems to have been especially neglected in work on

language-based assessment of mental illness," the authors of the

U.S. study wrote in a report published in PNAS, the Proceedings

of the National Academy of Sciences.

Previous research on social media posts had indicated that

people who frequently use first-person pronouns, such as I, me

or mine, and certain categories of words, such as

self-deprecating terms, are at higher risk for depression.

For the new study, researchers used an "off the shelf" AI

tool to analyze language in posts from 868 volunteers, including

equal numbers of Black and white adults who shared other

characteristics such as age and gender.

All participants also completed a validated questionnaire

used by healthcare providers to screen for depression.

The use of "I-talk" or self-focused attention, and

self-deprecation, self-criticism and feeling like an outsider

were related to depression exclusively for white individuals,

said study co-author Sharath Chandra Guntuku of the Center for

Insights to Outcomes at Penn Medicine.

"We were surprised that these language associations found in

numerous prior studies didn't apply across the board," Guntuku

said.

Social media data cannot be used to diagnose a patient with

depression, Guntuku acknowledged, but it could be used for risk

assessment of an individual or group.

An earlier study by his team analyzed language in social

media posts to evaluate communities' mental health during the

COVID-19 pandemic.

In patients with substance abuse disorders, language on

social media indicating depression has been shown to provide

insight into the likelihood of treatment dropout and relapse,

said Brenda Curtis of the U.S. National Institute on Drug Abuse

at the National Institutes of Health, who also worked on the

study.