Integrating Artificial Intelligence (AI) With Workforce Solutions for Sustainable Care: A Follow Up to Artificial Intelligence and Machine Learning (ML) Based Decision Support Systems in Mental Health

This integrative literature review examines the evolving role of artificial intelligence (AI) and machine learning (ML) based clinical decision support systems (CDSS) in mental health (MH) care, expanding on findings from a prior review (Higgins et al. 2023). Using and integrative review framework, a systematic search of six databases was conducted with a focus on primary research published between 2022 and 2024. Five studies met the inclusion criteria and were analysed for key themes, methodologies, and findings. The results reaffirm AI's potential to enhance MH care delivery by improving diagnostic accuracy, alleviating clinician workloads, and addressing missed care. New evidence highlights the importance of clinician trust, system transparency, and ethical concerns, including algorithmic bias and equity, particularly for vulnerable populations. Advancements in AI model complexity, such as multimodal learning systems, demonstrate improved predictive capacity but underscore the ongoing challenge of balancing interpretability with innovation. Workforce challenges, including clinician burnout and staffing shortages, persist as fundamental barriers that AI alone cannot resolve. The review not only confirms the findings from the first review but also adds new layers of complexity and understanding to the discourse on AI-based CDSS in MH care. While AI-driven CDSS holds significant promise for optimising MH care, sustainable improvements require the integration of AI solutions with systemic workforce enhancements. Future research should prioritise large-scale, longitudinal studies to ensure equitable, transparent, and effective implementation of AI in diverse clinical contexts. A balanced approach addressing both technological and workforce challenges remain critical for advancing mental health care delivery.