Kjell, O. N., Kjell, K., & Schwartz, H. A. (2023).
Psychiatry Research, 333, 115667.
Abstract
In this narrative review, we survey recent empirical evaluations of AI-based language assessments and present a case for the technology of large language models to be poised for changing standardized psychological assessment. Artificial intelligence has been undergoing a purported “paradigm shift” initiated by new machine learning models, large language models (e.g., BERT, LAMMA, and that behind ChatGPT). These models have led to unprecedented accuracy over most computerized language processing tasks, from web searches to automatic machine translation and question answering, while their dialogue-based forms, like ChatGPT have captured the interest of over a million users. The success of the large language model is mostly attributed to its capability to numerically represent words in their context, long a weakness of previous attempts to automate psychological assessment from language. While potential applications for automated therapy are beginning to be studied on the heels of chatGPT's success, here we present evidence that suggests, with thorough validation of targeted deployment scenarios, that AI's newest technology can move mental health assessment away from rating scales and to instead use how people naturally communicate, in language.
Highlights
• Artificial intelligence has been undergoing a purported “paradigm shift” initiated by new machine learning models, large language models.
• We review recent empirical evaluations of AI-based language assessments and present a case for the technology of large language models, that are used for chatGPT and BERT, to be poised for changing standardized psychological assessment.
• While potential applications for automated therapy are beginning to be studied on the heels of chatGPT's success, here we present evidence that suggests, with thorough validation of targeted deployment scenarios, that AI's newest technology can move mental health assessment away from rating scales and to instead use how people naturally communicate, in language.
Here are some thoughts:
The article underscores the transformative role of machine learning (ML) and artificial intelligence (AI) in psychological assessment, marking a significant shift in how psychologists approach their work. By integrating these technologies, assessments can become more accurate, efficient, and scalable, enabling psychologists to analyze vast amounts of data and uncover patterns that might otherwise go unnoticed. This is particularly important in improving diagnostic accuracy, as AI can help mitigate human bias and subjectivity, providing data-driven insights that complement clinical judgment. However, the adoption of these tools also raises critical ethical and practical considerations, such as ensuring client privacy, data security, and the responsible use of AI in alignment with professional standards.
As AI becomes more prevalent, the role of psychologists is evolving, requiring them to collaborate with these technologies by focusing on interpretation, contextual understanding, and therapeutic decision-making, while maintaining their unique human expertise.
Looking ahead, the article highlights emerging trends like natural language processing (NLP) for analyzing speech and text, as well as wearable devices for real-time behavioral and physiological data collection, offering psychologists innovative methods to enhance their practice. These advancements not only improve the precision of assessments but also pave the way for more personalized and timely interventions, ultimately supporting better mental health outcomes for clients.