Naomi Omeonga Wa Kayembe (U Nantes Law and Political Science) has posted “Clinical Affordance as a Framework for Barriers, Transitions, and Policy: A Use Case on AI and NLP Integration in Psychiatry” on SSRN. Here is the abstract:
The integration of Artificial Intelligence (AI) and Natural Language Processing (NLP) in psychiatry has significantly progressed, evolving from early feasibility studies to sophisticated transformer-based models capable of automating clinical assessments, symptom detection, and treatment monitoring. While these technologies hold promise for enhancing psychiatric care, their adoption remains limited due to translational barriers related to the accessibility and acceptability of digital health solutions.
This narrative review synthesizes foundational NLP contributions from the 2010s alongside recent advancements in AI-driven psychiatry, emphasizing both technical scalability and regulatory considerations. To systematize the variables influencing AI adoption in care practice, we introduce Clinical Affordance, a conceptual framework that evaluates the integration potential of AI tools through two interdependent dimensions: accessibility (practical and organizational fit) and acceptability (normative expectations).
Drawing from a selective literature review, we identify the main translational constraints affecting NLP deployment in psychiatry. Ranging from EHR system fragmentation to the burden of explainability mandates and uneven usage patterns, these challenges are analyzed through the lens of Clinical Affordance, with emphasis on their implications for clinical implementation. We further argue that the transition from clinical decision support systems (AI-CDSS) to autonomous medical treatment (AI-Treatment) is central to understanding risk allocation and liability in AI-assisted psychiatry. Finally, we assess how the COVID-19 pandemic impacted public trust in AI-driven mental health solutions, particularly in relation to surveillance and ethical governance.
The article concludes with policy recommendations aimed at reinforcing Clinical Affordance through outcome-based regulation, differentiated accountability, and data governance. By bridging technical innovation with contextual viability, the Clinical Affordance framework supports the sustainable integration of AI and NLP into psychiatric practice and offers a generalizable model for evaluating other digital health technologies.