Schizophrenia and bipolar disorder are serious mental health conditions that often begin in late teenage years or early adulthood. These illnesses can deeply affect a person’s thoughts, emotions, behavior, relationships, and ability to work or study.
Although treatments are available and many people can live stable lives with the right care, the most important step is getting the correct diagnosis as early as possible. Unfortunately, this is not easy.
In many cases, people first experience mild symptoms such as anxiety, depression, sleep problems, or trouble concentrating. These early signs can look like common mental health issues, so the underlying illness may not be recognized for years.
Research has shown that long delays between the first symptoms and the correct diagnosis can make treatment more difficult and reduce the chances of recovery. During this period, people may struggle in school, work, and relationships, and may not understand what is happening to them.
Doctors and mental health professionals therefore face a major challenge: how to identify people who are likely to develop schizophrenia or bipolar disorder before the illness becomes severe.
A new study from Aarhus University and Aarhus University Hospital in Denmark suggests that artificial intelligence could help solve this problem.
The research team, led by Professor Søren Dinesen Østergaard, used computer technology to analyze large amounts of medical information and look for patterns that might signal a future diagnosis. Their work was published in the medical journal JAMA Psychiatry.
The scientists studied electronic health records from 24,449 patients who had been treated for other mental health conditions such as anxiety and depression. These people did not yet have schizophrenia or bipolar disorder when the data were collected.
The researchers trained a computer program to examine more than one thousand pieces of information from each patient’s record. This included past diagnoses, medications, hospital visits, and written notes by doctors and therapists.
The goal was to create a system that could estimate whether a person might be diagnosed with schizophrenia or bipolar disorder within the next five years. If such a tool worked well, doctors could monitor high‑risk patients more closely, ask targeted questions, and start treatment earlier if symptoms appeared.
The results were encouraging but not perfect. For every 100 patients the system labeled as high risk, about 13 were actually diagnosed with schizophrenia or bipolar disorder within five years.
On the other hand, among those labeled as low risk, about 95 out of 100 did not develop these illnesses during that period. This means the system was better at identifying people who were unlikely to develop the disorders than those who were likely to develop them.
One of the most interesting findings was that the most useful clues came from the written notes made by healthcare staff rather than from diagnoses or prescriptions alone.
Certain words and phrases describing symptoms, such as social withdrawal, hearing voices, or being admitted to a psychiatric hospital, were strong warning signs. These details often capture subtle changes in behavior that numbers alone cannot show.
The computer model used in the study was relatively simple and focused mainly on counting how often certain words appeared. It did not fully understand the meaning of sentences or the context of the notes.
However, more advanced language models now exist that can understand full sentences and complex patterns in language. The researchers believe that using these newer tools could greatly improve accuracy in the future.
The study’s findings suggest that artificial intelligence could become a valuable assistant for mental health professionals. It would not replace doctors, but it could help them notice warning signs earlier and provide support sooner. Early treatment is known to improve outcomes, reduce hospitalizations, and help people maintain their daily lives.
However, the research also shows that the technology is not yet ready for routine clinical use. Predicting mental illness is extremely complex because each person’s experience is different, and symptoms can overlap with many other conditions.
There are also ethical questions about labeling someone as high risk before they become ill, which could cause anxiety or stigma if not handled carefully.
Overall, this study represents an important step toward earlier detection of severe mental disorders. It highlights how modern technology can uncover patterns hidden within large medical databases and turn them into practical tools for healthcare.
If future versions of the system become more accurate, they could shorten the time between the first symptoms and the correct diagnosis, giving patients a better chance at effective treatment and recovery.
In conclusion, the research offers hope but also calls for caution. Artificial intelligence shows promise in helping doctors identify people at risk of schizophrenia and bipolar disorder, yet it must be refined and carefully tested before being widely used.
The study reminds us that combining human expertise with advanced technology may be the key to improving mental health care in the years ahead.
If you care about health, please read studies that scientists find a core feature of depression and this metal in the brain strongly linked to depression.
For more health information, please see recent studies about drug for mental health that may harm the brain, and results showing this therapy more effective than ketamine in treating severe depression.
Copyright © 2026 Knowridge Science Report. All rights reserved.
