AI-based Large Language Models are Ready to Transform Psychological Health Assessment

Artificial intelligence-based (AI-based) language analysis has been undergoing a purported “paradigm shift” initiated by new machine learning models, large language models. These models, such as GPT3 or BERT, have led to unprecedented accuracies over most computerized language processing tasks such as web search, automatic machine translation, and question answering, while their chat-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 popular language assessment techniques that use “bag-of-words” or word count approaches. While many potential applications, from therapy to health information education, are beginning to be studied on the heels of chatGPT’s success, here we suggest these models are already ready to improve how we do psychological assessment. We present the case for AI's paradigm shift to large language models to be the missing piece for a parallel transformation in mental health assessment from the strong reliance on rating scale responses.