Addressing Workforce and Ethical Gaps in AI-Driven Mental Health Care: A Response to Higgins and Wilson

Artificial intelligence (AI)-based clinical decision support systems (CDSS) hold great promise for mental health (MH) care, offering opportunities to reduce clinician workload, improve diagnostic accuracy, and enhance patient monitoring. However, recent article, Integrating Artificial Intelligence (AI) With Workforce Solutions for Sustainable Care, highlights how ongoing staffing shortages and complex organisational dynamics can constrain AI's potential to resolve missed care. This letter builds on their review by emphasising two critical issues: (1) the persistent workforce gap, which undermines efforts to integrate AI effectively, and (2) the pressing need for robust ethical and regulatory frameworks to manage algorithmic bias and data fairness. Recent findings suggest that AI tools require human-AI partnerships, transparent accountability, and culturally adapted solutions to succeed in diverse and underserved populations. Large-scale, longitudinal studies, combined with sustained workforce development, remain essential. Addressing the interplay between technological advancement and systemic workforce barriers can ensure that AI-driven CDSS evolves into a truly equitable, evidence-based resource for mental health practitioners and patients alike.