AI-Enabled Self-Referral System may Bridge the Accessibility Gap for Ethnic and Gender Minorities in Mental Health Treatment

In a recent observational study published in Nature Medicine, researchers assessed the impact of an artificial intelligence (AI)-powered self-referral chatbot on diversity and volume of patient referrals in gender, ethnicity, and sexual orientation. They found that as compared to control services, the services utilizing the AI chatbot experienced a substantial increase in referrals, particularly among minorities, potentially owing to the human-free nature of the bot.

Mental health, acknowledged as a global priority by the World Health Organization, faces increased challenges across the global population. Limited access to mental healthcare continues to persist due to structural issues like underfunding and understaffing. Additionally, individuals with mental health problems often face barriers such as stigma, negative attitudes, and structural obstacles, especially those from minority and disadvantaged backgrounds. The initial step in mental healthcare involves seeking help and referrals, which are crucial for timely support and preventing adverse outcomes. However, evidence shows that individuals from minority groups encounter stronger barriers and stigma in accessing care.

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Felisa Laboro has been working with addiction and substance abuse businesses since early 2014. She has authored and published over 1,000 articles in the space. As a result of her work, over 1,500 people have been able to find treatment. She is passionate about helping people break free from alcohol or drug addiction and living a healthy life.

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