AI in Mental Health

How Does AI Enable Early Detection of Psychosis?

· 4 min read

How Does AI Enable Early Detection of Psychosis?

Author: Rasit Dinc

Introduction

Psychosis, a condition characterized by a disconnect from reality, presents a significant challenge to individuals and healthcare systems alike. Early detection is crucial for improving long-term outcomes, yet traditional diagnostic methods often rely on subjective assessments and the manifestation of overt symptoms. The advent of artificial intelligence (AI) offers a paradigm shift in mental healthcare, providing powerful new tools for identifying individuals at high risk of developing psychosis before the condition fully emerges [1]. By leveraging sophisticated algorithms, AI can analyze complex datasets to uncover subtle patterns that may be indicative of an impending psychotic episode, paving the way for preemptive interventions and more personalized care.

The Power of Machine Learning in Predictive Modeling

At the forefront of AI-driven early detection are machine learning (ML) models. These algorithms are trained on vast amounts of data, learning to identify predictive biomarkers associated with psychosis. A 2023 review in Translational Psychiatry highlights the diverse data sources being utilized, including neuroimaging, neurophysiological, genetic, and clinical features [2].

Machine learning (ML) has been a promising approach in the research of treatment outcome prediction in psychosis. [2]

Structural and functional magnetic resonance imaging (sMRI and fMRI) have proven particularly valuable. For instance, ML models can analyze fMRI data to detect alterations in brain connectivity and activity that precede the onset of psychosis. Similarly, sMRI data can reveal subtle changes in brain structure, such as reduced gray matter volume in specific regions, which have been linked to an increased risk of developing the disorder. By integrating these multi-modal data sources, ML models can create comprehensive risk profiles that are far more nuanced than any single measure alone.

Unlocking Insights from Language with NLP

Natural Language Processing (NLP), a specialized branch of AI, is another powerful tool for the early detection of psychosis. Language is a direct reflection of thought processes, and disruptions in language can be a key indicator of an underlying thought disorder. Disorganized speech, for example, is a hallmark symptom of schizophrenia, a common psychotic disorder.

A 2024 scoping review in the Journal of Personalized Medicine explores the various applications of NLP in schizophrenia research [3]. NLP algorithms can analyze transcribed speech or written text to identify subtle linguistic markers that may not be apparent to a human listener. These markers can include:

By quantifying these linguistic features, NLP provides an objective and scalable method for tracking changes in an individual's mental state over time, offering a non-invasive way to monitor for early signs of psychosis.

The Promise and Perils of Predictive Accuracy

While the potential of AI in early psychosis detection is immense, it is not without its challenges. A 2025 study in Frontiers in Psychiatry tested the generalizability of a machine learning model trained to predict conversion to psychosis and found that while the model performed above chance, its accuracy was not yet sufficient for clinical application [1]. This highlights a critical issue in AI-driven diagnostics: the need for robust, generalizable models that can perform accurately across diverse populations and clinical settings.

Achieving this will require large, high-quality datasets for training and validation, as well as a concerted effort to address issues of bias and fairness in algorithmic decision-making. Despite these hurdles, the rapid pace of innovation in AI and the growing body of research in this area offer hope for a future where psychosis can be detected and treated earlier and more effectively than ever before.

Conclusion

Artificial intelligence, through the application of machine learning and natural language processing, is poised to revolutionize the early detection of psychosis. By analyzing a wide range of data, from brain scans to speech patterns, AI can identify individuals at high risk with a level of precision that was previously unattainable. While further research is needed to refine these models and ensure their clinical utility, the progress made thus far represents a significant step forward in the quest to improve the lives of those affected by psychotic disorders.

References

[1] Smucny, J., Cannon, T. D., Bearden, C. E., Addington, J., Cadenhead, K. S., Cornblatt, B. A., ... & Carter, C. S. (2025). Predicting conversion to psychosis using machine learning: response to Cannon. Frontiers in Psychiatry, 15, 1520173. https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2024.1520173/full

[2] Del Fabro, L., Bondi, E., Serio, F., Maggioni, E., D’Agostino, A., & Brambilla, P. (2023). Machine learning methods to predict outcomes of pharmacological treatment in psychosis. Translational Psychiatry, 13(1), 75. https://www.nature.com/articles/s41398-023-02371-z

[3] Deneault, A., Dumais, A., Désilets, M., & Hudon, A. (2024). Natural Language Processing and Schizophrenia: A Scoping Review of Uses and Challenges. Journal of Personalized Medicine, 14(7), 744. https://pmc.ncbi.nlm.nih.gov/articles/PMC11278236/

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