Artificial Intelligence in Neurodiagnostic: Safeguarding Ethical Standards in Migrant Mental Health Care

Abstract

Artificial intelligence (AI) is rapidly reshaping neurodiagnostic, creating new opportunities for earlier and more accurate detection of mental health conditions. Yet, migrant communities especially those living in high-pressure environments continue to face significant barriers to care and are disproportionately affected by stress, depression, and anxiety stemming from occupational demands, cultural adjustment, and social isolation. This paper introduces a dualmodality AI framework that combines clinical screening data with brain imaging to evaluate mental health in migrant populations. The approach integrates DASS-21 survey responses, demographic and cognitive data with convolutional neural networks trained on MRI scans, demonstrating the potential for scalable and interpretable diagnostic support. At the heart of the framework are essential ethical commitments: fairness in model development, transparency in decisionmaking, cultural responsiveness in design, and unwavering respect for privacy. The paper emphasizes that the success of AI in migrant mental health care depends not only on technical performance but on building systems grounded in trust, accountability, and equitable access.

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