Prof Uses AI for Cultural Sensitivity in Mental Health Therapy

Published Date : 24-07-2025 

George Mason University professor Natasha Tonge is using artificial intelligence and natural language processing to uncover hidden cultural and social identifiers in therapy notes, aiming to improve patient care. 

A patient’s mental health therapy notes might seem straightforward, but George Mason University professor and clinical psychologist Natasha Tonge believes these notes can be mined for cultural and social identifiers—subtle, sometimes hidden, clues about a patient’s life and experience—that can help inform a patient’s care but are often missed in a busy clinical setting.

Tonge is leading a research project, “Leveraging AI/ML to Improve Cultural Preparedness of Mental Health Professionals,” that uses artificial intelligence and machine learning (AI/ML) to parse therapy notes to dig out those subtle clues. The project just received $64,000 from Morehouse School of Medicine on a subaward from the National Institutes of Health.

“The goal of this project is to make it easier to identify a taxonomy of the different types of cultural and social identifiers in therapy records that may be relevant for patient care,” said Tonge, who is a clinical health provider and the founder and director of George Mason’s Trust and Interpersonal Disclosure Lab, located at Fuse at Mason Square.

Examples of social identifiers include language that might give clues about a patient’s culture and community background, gender identity, socioeconomic status, and more, Tonge explained. Tonge first began thinking about the concept of gaps in cultural identifiers and data collection in the mental health field while working at a psychiatric rehabilitation center in St. Louis, where she discovered that Black patients confided to her that they were having trouble connecting with the center’s staff.

To possibly bridge this treatment gap, Tonge turned to AI. After anonymizing patient treatment notes, “I test different strategies for identifying finding identifiers in the text in a process known as labeling, or annotation,” she explained. The AI approach being utilized is a natural language processing pipeline. Once the manual labels are applied—for example, labeling a phrase like 

Frequently Asked Questions (FAQS): 

Q: What is the main goal of Natasha Tonge's research project?

A: The main goal of Natasha Tonge's research project is to use artificial intelligence and machine learning to identify cultural and social identifiers in therapy notes that can help improve patient care.

Q: How does the AI approach work in this project?

A: The AI approach involves using a natural language processing pipeline to label and identify cultural and social identifiers in therapy notes, which are then used to train a neural network to recognize these identifiers.

Q: What kind of social identifiers are being looked for in therapy notes?

A: Social identifiers include language that gives clues about a patient’s culture, community background, gender identity, socioeconomic status, and more.

Q: Who is working with Natasha Tonge on this project?

A: Natasha Tonge is working with George Mason graduate students in clinical psychology, a team of volunteers, and rising junior Griffin Perry, who is overseeing the de-identification process.

Q: What is the significance of the Center for Community and Mental Health (CCMH)?

A: The Center for Community and Mental Health (CCMH) is George Mason’s primary training clinic for doctoral candidates in clinical psychology, providing a wide range of therapy services to the community. 

More Related Topics :