Precision psychiatry combines psychiatry, precision medicine, and pharmacogenomics to devise personalized treatments for psychiatric disorders. The rise of Artificial Intelligence (AI) and machine learning technologies has made it possible to identify a multitude of biomarkers and genetic locations associated with these conditions.
AI and machine learning have strong potential in predicting the responses of patients to psychiatric drugs, such as antidepressants and lithium. Deep learning models, such as multi-layer feedforward neural networks, assimilate information on Single Nucleotide Polymorphisms (SNPs), demographics, and clinical data to produce highly accurate predictions on drug responses. Conventional machine learning techniques, like decision trees and random forests, also show great promise.
AI and machine learning technologies can predict future medical outcomes for psychiatric disorders based on current patient data. Tools using these technologies, such as Deep Patient, can predict diseases such as ADHD and schizophrenia with impressive accuracy. Other tools, such as DeepCare and Doctor AI, use recurrent neural networks to predict prognosis based on irregularly timed events recorded in Electronic Health Records (EHRs).
In diagnosing psychiatric disorders, AI and machine learning are increasingly used. For instance, these technologies can distinguish between healthy individuals and those suffering from Alzheimer’s, autism, and schizophrenia. Such diagnoses are achieved using neuroimaging data and have shown significantly high accuracy.
Despite significant strides made possible by AI and machine learning, several limitations persist. Many AI and machine learning studies have small sample sizes, which poses a risk of overfitting and restricts generalizability. Data heterogeneity and missing data complicates analyses. Reliance on retrospective data necessitates long-term monitoring of disease trajectories.
Going forward, research should focus on larger and prospective studies, better data harmonization, and predictive models that are generalizable. This would boost the field’s robustness and applicability.
The emerging field of precision psychiatry is promising for the diagnosis and treatment of psychiatric disorders. It leverages AI and machine learning technologies for personalized treatment, prediction of prognosis, and detection of biomarkers. Future research should strive to integrate multi-omics and neuroimaging data to deepen understanding of psychiatric disorders.
The development of new AI frameworks, especially deep learning algorithms, could revolutionize public and global health in the future. As technologies advance and knowledge is expanded, it is likely that pre-treatment prediction tests will be implemented in clinical care on a large scale, to refine biomarkers and clinical factors, and tailor treatment plans to individual patients.
