Scientists create AI language models that identify signs of schizophrenia

The scientists utilised an AI language model that has been trained on enormous quantities of internet material to represent the meaning of words in a comparable fashion to humans to examine the answers supplied by participants.

Washington DC (US): Scientists have created new tools based on AI language models that may identify tiny signs in the speech of individuals with schizophrenia.

The study, published in PNAS, seeks to explore how automated language analysis may assist clinicians and scientists in diagnosing and assessing psychiatric illnesses. Currently, mental diagnosis is nearly exclusively reliant on conversations with patients and people close to them, with blood tests and brain scans playing only a minor role.

This lack of clarity, however, precludes a more comprehensive understanding of the causes of mental disease and the monitoring of treatment.

The researchers invited 26 people with schizophrenia and 26 control participants to complete two verbal fluency tasks in which they were asked to name as many words as they could from the category "animals" or begin with the letter "p" in five minutes.

The scientists utilised an AI language model that has been trained on enormous quantities of internet material to represent the meaning of words in a comparable fashion to humans to examine the answers supplied by participants. They investigated whether the AI model could predict the phrases that people spontaneously recalled and whether this predictability was impaired in patients with schizophrenia.

They found that the answers given by control participants were indeed more predictable by the AI model than those generated by people with schizophrenia and that this difference was largest in patients with more severe symptoms.

The researchers think that this difference might have to do with the way the brain learns relationships between memories and ideas, and stores this information in so called 'cognitive maps'. They find support for this theory in a second part of the same study where the authors used brain scanning to measure brain activity in parts of the brain involved in learning and storing these 'cognitive maps'.

Lead author, Dr Matthew Nour (UCL Queen Square Institute of Neurology and University of Oxford), said: "Until very recently, the automatic analysis of language has been out of reach of doctors and scientists. However, with the advent of artificial intelligence (AI) language models such as ChatGPT, this situation is changing.

"This work shows the potential of applying AI language models to psychiatry - a medical field intimately related to language and meaning."

Schizophrenia is a debilitating and common psychiatric disorder that affects around 24 million people worldwide and over 685,000 people in the UK.

According to the NHS, symptoms of the condition may include hallucinations, delusions, confused thoughts and changes in behaviour.

The team from UCL and Oxford now plan to use this technology in a larger sample of patients, across more diverse speech settings, to test whether it might prove useful in the clinic.

Dr Nour said: "We are entering a very exciting time in neuroscience and mental health research. By combining state-of-the-art AI language models and brain-scanning technology, we are beginning to uncover how meaning is constructed in the brain, and how this might go awry in psychiatric disorders. There is enormous interest in using AI language models in medicine. If these tools prove safe and robust, I expect they will begin to be deployed in the clinic within the next decade."