Title
At the Implementation Cliff: Precision Psychiatry and the Limits of Clinical-Level Intervention
Abstract
Mental disorders account for approximately 16% of the global burden of disease. Projections based on WHO data indicate an escalation across the Global South by 2050. The high global burden of disease can be attributed, in part, to limitations in the current social-structural, clinical, and conceptual approaches to mental health. AI-based precision psychiatry promises to improve psychiatry through personalised treatment, diagnosis, and prognosis, although systematic reviews report a more than 99% implementation failure rate.
This article has argued that the implementation of AI-based precision psychiatry will remain difficult as long as models designed to intervene at the clinical level are deployed across a broader psychiatric ecosystem whose conceptual and structural constraint they were never built to navigate.
During the development of precision psychiatry models, clinical operations are often alienated from the conceptual and social structural elements that shape how clinical psychiatry functions. Mental health care majorly operates across three embedded layers: Social-structural (systems, institutions, and resources through which mental health is delivered), Conceptual (how mental illness is locally understood), and Clinical (interface between patients and clinical psychiatry for purposes of diagnosis, treatment, and prediction of potential health outcomes). Precision psychiatry is designed to strictly intervene at the clinical level, yet it’s implemented across all layers. We propose that development and implementation should instead be preceded by reflection on the actual setup of clinical psychiatry, making considerations for whether social-structural conditions of the target population enable availability, accessibility, and adoption of developed models and whether local conceptualisations of mental illness are compatible with the model’s Knowledge.
The Global South context, with diverse conceptualisations of mental health and social-structural limitations, provides a clearer example of how elements within these layers can interconnect to impede the implementation of precision psychiatry. Without reflection on these layers, most of the developed models will fall at the implementation cliff, leading to wastage of already scarce resources.
About Stella
Stella Namuganza holds a bachelor’s degree in nursing and a master’s degree in clinical research. She is currently pursuing a PhD on the ethical, legal, and social aspects (ELSA) of digital health technologies. She works as a Research Associate at the University of Tübingen with the Research Hub Neuroethics (RHUNE) project at the Hertie Institute for AI in Brain Health. Her research employs meta-research approaches to investigate ELSA issues at the intersection of artificial intelligence and neuroscience, with a particular focus on the responsible development and implementation of emerging digital health technologies.