How can AI models be adapted to incorporate cultural sensitivity in diagnosing and treating mental health conditions, considering Western and non-Western cultural contexts?
Abstract
Artificial Intelligence (AI) is revolutionizing mental health care with advanced diagnostic tools, yet these systems often suffer from cultural biases in their design and training data. This research examines how AI can be adapted for cultural sensitivity in diagnosing and treating mental health conditions, focusing on both Western and non-Western contexts. Key barriers to seeking mental health help—such as social stigma, financial constraints, and lack of awareness—are identified, along with the influence of cultural factors like dreams and religious beliefs on mental health perceptions. Through a comparative analysis, the study reveals disparities in mental health understanding across cultures and underscores the need to integrate non Western perspectives into AI systems. The findings provide recommendations for developing culturally responsive AI tools, aiming to create technologies that better address the diverse needs of individuals from various cultural backgrounds, ultimately contributing to more equitable and effective mental health care.
Key Words: Cultural sensitivity, Mental health diagnostics, AI adaptation, Western and non-Western contexts
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Data Availability Statement
The data that support the findings of this study are included within the article. However, the raw data collected during the study are not publicly available due to confidentiality agreements and participant privacy.
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