Knowledge-Infused Learning for Developing a Mental Health Diagnostic Copilot in Healthcare Systems

Abstract Healthcare diagnostics, especially in underserved communities, faces critical gaps in accessibility and accuracy. African Americans experience significant disparities in mental health care, often receiving delayed or inadequate treatment. This research proposes a diagnostic copilot, an AI-powered assistant designed to work alongside healthcare professionals. Using Knowledge-Infused Learning (KIL) and multi-turn conversations, the system integrates clinical knowledge and patient input to deliver actionable, explainable diagnoses in real-time. By engaging with both patients and clinicians, the copilot aims to reduce disparities, enhance trust, and improve diagnostic accuracy in mental health care.